research topics for nlp

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Disentanglement

Graph representation learning, sentence embeddings.

Network Embedding

Classification.

Text Classification

Graph Classification

Audio Classification

Medical Image Classification

Language modelling.

Long-range modeling

Protein language model, sentence pair modeling, deep hashing, table retrieval, question answering.

Open-Ended Question Answering

Open-Domain Question Answering

Conversational question answering.

Answer Selection

Translation, image generation.

Image-to-Image Translation

Image Inpainting

Text-to-Image Generation

Conditional Image Generation

Data augmentation.

Image Augmentation

Text Augmentation

Machine translation.

Transliteration

Bilingual lexicon induction.

Multimodal Machine Translation

Unsupervised Machine Translation

Text generation.

Dialogue Generation

Data-to-Text Generation

Multi-Document Summarization

Text style transfer.

Topic Models

Document Classification

Sentence Classification

Emotion Classification

2d semantic segmentation, image segmentation.

Scene Parsing

Reflection Removal

Visual question answering (vqa).

Visual Question Answering

Machine Reading Comprehension

Chart Question Answering

Embodied Question Answering

Named entity recognition (ner).

Nested Named Entity Recognition

Chinese named entity recognition, few-shot ner, sentiment analysis.

Aspect-Based Sentiment Analysis (ABSA)

Multimodal Sentiment Analysis

Aspect Sentiment Triplet Extraction

Twitter Sentiment Analysis

Few-shot learning.

One-Shot Learning

Few-Shot Semantic Segmentation

Cross-domain few-shot.

Unsupervised Few-Shot Learning

Word embeddings.

Learning Word Embeddings

Multilingual Word Embeddings

Embeddings evaluation, contextualised word representations, optical character recognition (ocr).

Active Learning

Handwriting Recognition

Handwritten digit recognition, irregular text recognition, continual learning.

Class Incremental Learning

Continual named entity recognition, unsupervised class-incremental learning, text summarization.

Abstractive Text Summarization

Document summarization, opinion summarization, information retrieval.

Passage Retrieval

Cross-lingual information retrieval, table search, relation extraction.

Relation Classification

Document-level relation extraction, joint entity and relation extraction, temporal relation extraction, link prediction.

Inductive Link Prediction

Dynamic link prediction, hyperedge prediction, anchor link prediction, natural language inference.

Answer Generation

Visual Entailment

Cross-lingual natural language inference, reading comprehension.

Intent Recognition

Implicit relations, large language model, active object detection, emotion recognition.

Speech Emotion Recognition

Emotion Recognition in Conversation

Multimodal Emotion Recognition

Emotion-cause pair extraction, natural language understanding, vietnamese social media text processing.

Emotional Dialogue Acts

Semantic textual similarity.

Paraphrase Identification

Cross-Lingual Semantic Textual Similarity

Image captioning.

3D dense captioning

Controllable image captioning, aesthetic image captioning.

Relational Captioning

Event extraction, event causality identification, zero-shot event extraction, dialogue state tracking, task-oriented dialogue systems.

Visual Dialog

Dialogue understanding, semantic parsing.

AMR Parsing

Semantic dependency parsing, drs parsing, ucca parsing, coreference resolution, coreference-resolution, cross document coreference resolution, in-context learning, semantic similarity, conformal prediction.

Text Simplification

Music Source Separation

Audio source separation.

Decision Making Under Uncertainty

Sentence Embedding

Sentence compression, joint multilingual sentence representations, sentence embeddings for biomedical texts, code generation.

Code Translation

Code Documentation Generation

Class-level code generation, library-oriented code generation, specificity, dependency parsing.

Transition-Based Dependency Parsing

Prepositional phrase attachment, unsupervised dependency parsing, cross-lingual zero-shot dependency parsing, information extraction, extractive summarization, temporal information extraction, low resource named entity recognition, cross-lingual, cross-lingual transfer, cross-lingual document classification.

Cross-Lingual Entity Linking

Cross-language text summarization, response generation, common sense reasoning.

Physical Commonsense Reasoning

Riddle sense, anachronisms, instruction following, visual instruction following, memorization, data integration.

Entity Alignment

Entity Resolution

Table annotation, entity linking.

Prompt Engineering

Visual Prompting

Question generation, poll generation.

Topic coverage

Dynamic topic modeling, part-of-speech tagging.

Unsupervised Part-Of-Speech Tagging

Mathematical reasoning.

Math Word Problem Solving

Formal logic, geometry problem solving, abstract algebra, abuse detection, hate speech detection, open information extraction.

Hope Speech Detection

Hate speech normalization, hate speech detection crisishatemm benchmark, data mining.

Argument Mining

Opinion Mining

Subgroup discovery, cognitive diagnosis, parallel corpus mining, word sense disambiguation.

Word Sense Induction

Language identification, dialect identification, native language identification, few-shot relation classification, implicit discourse relation classification, cause-effect relation classification, bias detection, selection bias, fake news detection, relational reasoning.

Semantic Role Labeling

Predicate Detection

Semantic role labeling (predicted predicates).

Textual Analogy Parsing

Slot filling.

Zero-shot Slot Filling

Extracting covid-19 events from twitter, grammatical error correction.

Grammatical Error Detection

Text matching, document text classification, learning with noisy labels, multi-label classification of biomedical texts, political salient issue orientation detection, pos tagging, deep clustering, trajectory clustering, deep nonparametric clustering, nonparametric deep clustering, spoken language understanding, dialogue safety prediction, stance detection, zero-shot stance detection, few-shot stance detection, stance detection (us election 2020 - biden), stance detection (us election 2020 - trump), multi-modal entity alignment, intent detection.

Open Intent Detection

Word similarity, text-to-speech synthesis.

Prosody Prediction

Zero-shot multi-speaker tts, zero-shot cross-lingual transfer, cross-lingual ner, fact verification, intent classification.

Language Acquisition

Grounded language learning, document ai, document understanding, entity typing.

Entity Typing on DH-KGs

Constituency parsing.

Constituency Grammar Induction

Self-learning, cross-modal retrieval, image-text matching, multilingual cross-modal retrieval.

Zero-shot Composed Person Retrieval

Cross-modal retrieval on rsitmd, ad-hoc information retrieval, document ranking.

Word Alignment

Open-domain dialog, dialogue evaluation, novelty detection, model editing, knowledge editing, multimodal deep learning, multimodal text and image classification, multi-label text classification, discourse parsing, discourse segmentation, connective detection.

Text-based Image Editing

Text-guided-image-editing.

Zero-Shot Text-to-Image Generation

Concept alignment, conditional text-to-image synthesis.

Shallow Syntax

De-identification, privacy preserving deep learning, sarcasm detection.

Explanation Generation

Lemmatization, morphological analysis.

Session Search

Aspect Extraction

Extract aspect, aspect category sentiment analysis.

Aspect-oriented Opinion Extraction

Aspect-Category-Opinion-Sentiment Quadruple Extraction

Molecular representation.

Chinese Word Segmentation

Handwritten chinese text recognition, chinese spelling error correction, chinese zero pronoun resolution, offline handwritten chinese character recognition, entity disambiguation, text-to-video generation, text-to-video editing, subject-driven video generation, conversational search, source code summarization, method name prediction, speech-to-text translation, simultaneous speech-to-text translation, text clustering.

Short Text Clustering

Open Intent Discovery

Authorship attribution, keyphrase extraction, linguistic acceptability.

Column Type Annotation

Cell entity annotation, columns property annotation, row annotation, abusive language.

Visual Storytelling

KG-to-Text Generation

Unsupervised KG-to-Text Generation

Few-shot text classification, zero-shot out-of-domain detection, term extraction, text2text generation, keyphrase generation, figurative language visualization, sketch-to-text generation, protein folding, phrase grounding, grounded open vocabulary acquisition, deep attention, morphological inflection, word translation, multilingual nlp, spam detection, context-specific spam detection, traditional spam detection, summarization, unsupervised extractive summarization, query-focused summarization.

Natural Language Transduction

Knowledge base population, conversational response selection, cross-lingual word embeddings, text annotation, passage ranking, image-to-text retrieval, news classification, key information extraction, biomedical information retrieval.

SpO2 estimation

Graph-to-sequence, authorship verification.

Keyword Extraction

Sentence summarization, unsupervised sentence summarization, automated essay scoring, story generation, temporal processing, timex normalization, document dating, meme classification, hateful meme classification, multimodal association, multimodal generation, morphological tagging, nlg evaluation, key point matching, component classification, argument pair extraction (ape), claim extraction with stance classification (cesc), claim-evidence pair extraction (cepe), weakly supervised classification, weakly supervised data denoising, entity extraction using gan.

Rumour Detection

Semantic composition.

Sentence Ordering

Comment generation.

Lexical Simplification

Token classification, toxic spans detection.

Blackout Poetry Generation

Semantic retrieval, subjectivity analysis.

Taxonomy Learning

Taxonomy expansion, hypernym discovery, conversational response generation.

Personalized and Emotional Conversation

Passage re-ranking, review generation, sentence-pair classification, emotional intelligence, dark humor detection, lexical normalization, pronunciation dictionary creation, negation detection, negation scope resolution, question similarity, medical question pair similarity computation, goal-oriented dialog, user simulation, intent discovery, propaganda detection, propaganda span identification, propaganda technique identification, lexical analysis, lexical complexity prediction, question rewriting, punctuation restoration, reverse dictionary, humor detection.

Meeting Summarization

Table-based fact verification, legal reasoning, pretrained multilingual language models, formality style transfer, semi-supervised formality style transfer, word attribute transfer, attribute value extraction, diachronic word embeddings, persian sentiment analysis, clinical concept extraction.

Clinical Information Retreival

Constrained clustering.

Only Connect Walls Dataset Task 1 (Grouping)

Incremental constrained clustering, aspect category detection, dialog act classification, extreme summarization.

Hallucination Evaluation

Long-context understanding, recognizing emotion cause in conversations.

Causal Emotion Entailment

Nested Mention Recognition

Relationship extraction (distant supervised), binary classification, llm-generated text detection, cancer-no cancer per breast classification, cancer-no cancer per image classification, suspicous (birads 4,5)-no suspicous (birads 1,2,3) per image classification, cancer-no cancer per view classification, clickbait detection, decipherment, semantic entity labeling, text compression, handwriting verification, bangla spelling error correction, ccg supertagging, gender bias detection, linguistic steganography, probing language models, toponym resolution.

Timeline Summarization

Multimodal abstractive text summarization, reader-aware summarization, code repair, thai word segmentation, stock prediction, text-based stock prediction, event-driven trading, pair trading.

Face to Face Translation

Multimodal lexical translation, explanatory visual question answering, vietnamese visual question answering, aggression identification, arabic text diacritization, commonsense causal reasoning, fact selection, suggestion mining, temporal relation classification, vietnamese datasets, vietnamese word segmentation, arabic sentiment analysis, aspect category polarity, complex word identification, cross-lingual bitext mining, morphological disambiguation, scientific document summarization, lay summarization, text attribute transfer.

Image-guided Story Ending Generation

Speculation detection, speculation scope resolution, abstract argumentation, dialogue rewriting, logical reasoning reading comprehension.

Unsupervised Sentence Compression

Sign language production, stereotypical bias analysis, temporal tagging, anaphora resolution, bridging anaphora resolution.

Abstract Anaphora Resolution

Hope speech detection for english, hope speech detection for malayalam, hope speech detection for tamil, hidden aspect detection, latent aspect detection, chinese spell checking, cognate prediction, japanese word segmentation, memex question answering, multi-agent integration, polyphone disambiguation, spelling correction, table-to-text generation.

KB-to-Language Generation

Text anonymization, vietnamese language models, zero-shot sentiment classification, conditional text generation, contextualized literature-based discovery, multimedia generative script learning, image-sentence alignment, open-world social event classification, personality generation, personality alignment, action parsing, author attribution, binary condescension detection, conversational web navigation, croatian text diacritization, czech text diacritization, definition modelling, document-level re with incomplete labeling, domain labelling, french text diacritization, hungarian text diacritization, irish text diacritization, latvian text diacritization, misogynistic aggression identification, morpheme segmentaiton, multi-label condescension detection, news annotation, open relation modeling, personality recognition in conversation.

Reading Order Detection

Record linking, role-filler entity extraction, romanian text diacritization, slovak text diacritization, spanish text diacritization, syntax representation, text-to-video search, turkish text diacritization, turning point identification, twitter event detection.

Vietnamese Text Diacritization

Zero-shot machine translation.

Conversational Sentiment Quadruple Extraction

Attribute extraction, legal outcome extraction, automated writing evaluation, chemical indexing, clinical assertion status detection.

Coding Problem Tagging

Collaborative plan acquisition, commonsense reasoning for rl, context query reformulation.

Variable Disambiguation

Cross-lingual text-to-image generation, crowdsourced text aggregation.

Description-guided molecule generation

Multi-modal Dialogue Generation

Page stream segmentation.

Email Thread Summarization

Emergent communications on relations, emotion detection and trigger summarization, extractive tags summarization.

Hate Intensity Prediction

Hate span identification, job prediction, joint entity and relation extraction on scientific data, joint ner and classification, literature mining, math information retrieval, meme captioning, multi-grained named entity recognition, multilingual machine comprehension in english hindi, multimodal text prediction, negation and speculation cue detection, negation and speculation scope resolution, only connect walls dataset task 2 (connections), overlapping mention recognition, paraphrase generation, multilingual paraphrase generation, phrase ranking, phrase tagging, phrase vector embedding, poem meters classification, query wellformedness.

Question-Answer categorization

Readability optimization, reliable intelligence identification, sentence completion, hurtful sentence completion, speaker attribution in german parliamentary debates (germeval 2023, subtask 1), text effects transfer, text-variation, vietnamese aspect-based sentiment analysis, sentiment dependency learning, vietnamese natural language understanding, web page tagging, workflow discovery, incongruity detection, multi-word expression embedding, multi-word expression sememe prediction, trustable and focussed llm generated content, pcl detection, semeval-2022 task 4-1 (binary pcl detection), semeval-2022 task 4-2 (multi-label pcl detection), automatic writing, complaint comment classification, counterspeech detection, extractive text summarization, face selection, job classification, multi-lingual text-to-image generation, multlingual neural machine translation, optical charater recogntion, bangla text detection, question to declarative sentence, relation mention extraction.

Tweet-Reply Sentiment Analysis

Vietnamese parsing.

Natural Language Processing

Introduction.

Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications like text generators that compose coherent essays, chatbots that fool people into thinking they’re sentient, and text-to-image programs that produce photorealistic images of anything you can describe. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output.

What is Natural Language Processing (NLP)

Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is written, spoken, and organized. It evolved from computational linguistics, which uses computer science to understand the principles of language, but rather than developing theoretical frameworks, NLP is an engineering discipline that seeks to build technology to accomplish useful tasks. NLP can be divided into two overlapping subfields: natural language understanding (NLU), which focuses on semantic analysis or determining the intended meaning of text, and natural language generation (NLG), which focuses on text generation by a machine. NLP is separate from — but often used in conjunction with — speech recognition, which seeks to parse spoken language into words, turning sound into text and vice versa.

Why Does Natural Language Processing (NLP) Matter?

NLP is an integral part of everyday life and becoming more so as language technology is applied to diverse fields like retailing (for instance, in customer service chatbots) and medicine (interpreting or summarizing electronic health records). Conversational agents such as Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers. The most sophisticated such agents — such as GPT-3, which was recently opened for commercial applications — can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results , and social networks like Facebook use it to detect and filter hate speech . 

NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society.

What is Natural Language Processing (NLP) Used For?

NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. 

Here are 11 tasks that can be solved by NLP:

• Sentiment analysis is the process of classifying the emotional intent of text. Generally, the input to a sentiment classification model is a piece of text, and the output is the probability that the sentiment expressed is positive, negative, or neutral. Typically, this probability is based on either hand-generated features, word n-grams, TF-IDF features, or using deep learning models to capture sequential long- and short-term dependencies. Sentiment analysis is used to classify customer reviews on various online platforms as well as for niche applications like identifying signs of mental illness in online comments.

• Toxicity classification is a branch of sentiment analysis where the aim is not just to classify hostile intent but also to classify particular categories such as threats, insults, obscenities, and hatred towards certain identities. The input to such a model is text, and the output is generally the probability of each class of toxicity. Toxicity classification models can be used to moderate and improve online conversations by silencing offensive comments , detecting hate speech , or scanning documents for defamation . 
• Machine translation automates translation between different languages. The input to such a model is text in a specified source language, and the output is the text in a specified target language. Google Translate is perhaps the most famous mainstream application. Such models are used to improve communication between people on social-media platforms such as Facebook or Skype. Effective approaches to machine translation can distinguish between words with similar meanings . Some systems also perform language identification; that is, classifying text as being in one language or another. 
• Named entity recognition aims to extract entities in a piece of text into predefined categories such as personal names, organizations, locations, and quantities. The input to such a model is generally text, and the output is the various named entities along with their start and end positions. Named entity recognition is useful in applications such as summarizing news articles and combating disinformation . For example, here is what a named entity recognition model could provide: 

• Spam detection is a prevalent binary classification problem in NLP, where the purpose is to classify emails as either spam or not. Spam detectors take as input an email text along with various other subtexts like title and sender’s name. They aim to output the probability that the mail is spam. Email providers like Gmail use such models to provide a better user experience by detecting unsolicited and unwanted emails and moving them to a designated spam folder. 
• Grammatical error correction models encode grammatical rules to correct the grammar within text. This is viewed mainly as a sequence-to-sequence task, where a model is trained on an ungrammatical sentence as input and a correct sentence as output. Online grammar checkers like Grammarly and word-processing systems like Microsoft Word use such systems to provide a better writing experience to their customers. Schools also use them to grade student essays . 
• Topic modeling is an unsupervised text mining task that takes a corpus of documents and discovers abstract topics within that corpus. The input to a topic model is a collection of documents, and the output is a list of topics that defines words for each topic as well as assignment proportions of each topic in a document. Latent Dirichlet Allocation (LDA), one of the most popular topic modeling techniques, tries to view a document as a collection of topics and a topic as a collection of words. Topic modeling is being used commercially to help lawyers find evidence in legal documents . 
• Autocomplete predicts what word comes next, and autocomplete systems of varying complexity are used in chat applications like WhatsApp. Google uses autocomplete to predict search queries. One of the most famous models for autocomplete is GPT-2, which has been used to write articles , song lyrics , and much more. 
• Database query: We have a database of questions and answers, and we would like a user to query it using natural language. 
• Conversation generation: These chatbots can simulate dialogue with a human partner. Some are capable of engaging in wide-ranging conversations . A high-profile example is Google’s LaMDA, which provided such human-like answers to questions that one of its developers was convinced that it had feelings .
• Information retrieval finds the documents that are most relevant to a query. This is a problem every search and recommendation system faces. The goal is not to answer a particular query but to retrieve, from a collection of documents that may be numbered in the millions, a set that is most relevant to the query. Document retrieval systems mainly execute two processes: indexing and matching. In most modern systems, indexing is done by a vector space model through Two-Tower Networks, while matching is done using similarity or distance scores. Google recently integrated its search function with a multimodal information retrieval model that works with text, image, and video data.

• Extractive summarization focuses on extracting the most important sentences from a long text and combining these to form a summary. Typically, extractive summarization scores each sentence in an input text and then selects several sentences to form the summary.
• Abstractive summarization produces a summary by paraphrasing. This is similar to writing the abstract that includes words and sentences that are not present in the original text. Abstractive summarization is usually modeled as a sequence-to-sequence task, where the input is a long-form text and the output is a summary.
• Multiple choice: The multiple-choice question problem is composed of a question and a set of possible answers. The learning task is to pick the correct answer. 
• Open domain : In open-domain question answering, the model provides answers to questions in natural language without any options provided, often by querying a large number of texts.

How Does Natural Language Processing (NLP) Work?

NLP models work by finding relationships between the constituent parts of language — for example, the letters, words, and sentences found in a text dataset. NLP architectures use various methods for data preprocessing, feature extraction, and modeling. Some of these processes are: 

• Stemming and lemmatization : Stemming is an informal process of converting words to their base forms using heuristic rules. For example, “university,” “universities,” and “university’s” might all be mapped to the base univers . (One limitation in this approach is that “universe” may also be mapped to univers , even though universe and university don’t have a close semantic relationship.) Lemmatization is a more formal way to find roots by analyzing a word’s morphology using vocabulary from a dictionary. Stemming and lemmatization are provided by libraries like spaCy and NLTK. 
• Sentence segmentation breaks a large piece of text into linguistically meaningful sentence units. This is obvious in languages like English, where the end of a sentence is marked by a period, but it is still not trivial. A period can be used to mark an abbreviation as well as to terminate a sentence, and in this case, the period should be part of the abbreviation token itself. The process becomes even more complex in languages, such as ancient Chinese, that don’t have a delimiter that marks the end of a sentence. 
• Stop word removal aims to remove the most commonly occurring words that don’t add much information to the text. For example, “the,” “a,” “an,” and so on.
• Tokenization splits text into individual words and word fragments. The result generally consists of a word index and tokenized text in which words may be represented as numerical tokens for use in various deep learning methods. A method that instructs language models to ignore unimportant tokens can improve efficiency.  

• Bag-of-Words: Bag-of-Words counts the number of times each word or n-gram (combination of n words) appears in a document. For example, below, the Bag-of-Words model creates a numerical representation of the dataset based on how many of each word in the word_index occur in the document. 

• Term Frequency: How important is the word in the document?

TF(word in a document)= Number of occurrences of that word in document / Number of words in document

• Inverse Document Frequency: How important is the term in the whole corpus?

IDF(word in a corpus)=log(number of documents in the corpus / number of documents that include the word)

A word is important if it occurs many times in a document. But that creates a problem. Words like “a” and “the” appear often. And as such, their TF score will always be high. We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. The TF-IDF score of a term is the product of TF and IDF. 

• Word2Vec , introduced in 2013 , uses a vanilla neural network to learn high-dimensional word embeddings from raw text. It comes in two variations: Skip-Gram, in which we try to predict surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which tries to predict the target word from surrounding words. After discarding the final layer after training, these models take a word as input and output a word embedding that can be used as an input to many NLP tasks. Embeddings from Word2Vec capture context. If particular words appear in similar contexts, their embeddings will be similar.
• GLoVE is similar to Word2Vec as it also learns word embeddings, but it does so by using matrix factorization techniques rather than neural learning. The GLoVE model builds a matrix based on the global word-to-word co-occurrence counts. 
• Numerical features extracted by the techniques described above can be fed into various models depending on the task at hand. For example, for classification, the output from the TF-IDF vectorizer could be provided to logistic regression, naive Bayes, decision trees, or gradient boosted trees. Or, for named entity recognition, we can use hidden Markov models along with n-grams. 
• Deep neural networks typically work without using extracted features, although we can still use TF-IDF or Bag-of-Words features as an input. 
• Language Models : In very basic terms, the objective of a language model is to predict the next word when given a stream of input words. Probabilistic models that use Markov assumption are one example:

P(W n )=P(W n |W n−1 )

Deep learning is also used to create such language models. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. They can then be fine-tuned for a particular task. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines . 

Top Natural Language Processing (NLP) Techniques

Most of the NLP tasks discussed above can be modeled by a dozen or so general techniques. It’s helpful to think of these techniques in two categories: Traditional machine learning methods and deep learning methods. 

Traditional Machine learning NLP techniques: 

• Logistic regression is a supervised classification algorithm that aims to predict the probability that an event will occur based on some input. In NLP, logistic regression models can be applied to solve problems such as sentiment analysis, spam detection, and toxicity classification.
• Naive Bayes is a supervised classification algorithm that finds the conditional probability distribution P(label | text) using the following Bayes formula:

P(label | text) = P(label) x P(text|label) / P(text) 

and predicts based on which joint distribution has the highest probability. The naive assumption in the Naive Bayes model is that the individual words are independent. Thus: 

P(text|label) = P(word_1|label)*P(word_2|label)*…P(word_n|label)

In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code . 

• Decision trees are a class of supervised classification models that split the dataset based on different features to maximize information gain in those splits.

• Latent Dirichlet Allocation (LDA) is used for topic modeling. LDA tries to view a document as a collection of topics and a topic as a collection of words. LDA is a statistical approach. The intuition behind it is that we can describe any topic using only a small set of words from the corpus.
• Hidden Markov models : Markov models are probabilistic models that decide the next state of a system based on the current state. For example, in NLP, we might suggest the next word based on the previous word. We can model this as a Markov model where we might find the transition probabilities of going from word1 to word2, that is, P(word1|word2). Then we can use a product of these transition probabilities to find the probability of a sentence. The hidden Markov model (HMM) is a probabilistic modeling technique that introduces a hidden state to the Markov model. A hidden state is a property of the data that isn’t directly observed. HMMs are used for part-of-speech (POS) tagging where the words of a sentence are the observed states and the POS tags are the hidden states. The HMM adds a concept called emission probability; the probability of an observation given a hidden state. In the prior example, this is the probability of a word, given its POS tag. HMMs assume that this probability can be reversed: Given a sentence, we can calculate the part-of-speech tag from each word based on both how likely a word was to have a certain part-of-speech tag and the probability that a particular part-of-speech tag follows the part-of-speech tag assigned to the previous word. In practice, this is solved using the Viterbi algorithm.

Deep learning NLP Techniques: 

• Convolutional Neural Network (CNN): The idea of using a CNN to classify text was first presented in the paper “ Convolutional Neural Networks for Sentence Classification ” by Yoon Kim. The central intuition is to see a document as an image. However, instead of pixels, the input is sentences or documents represented as a matrix of words.

• Recurrent Neural Network (RNN) : Many techniques for text classification that use deep learning process words in close proximity using n-grams or a window (CNNs). They can see “New York” as a single instance. However, they can’t capture the context provided by a particular text sequence. They don’t learn the sequential structure of the data, where every word is dependent on the previous word or a word in the previous sentence. RNNs remember previous information using hidden states and connect it to the current task. The architectures known as Gated Recurrent Unit (GRU) and long short-term memory (LSTM) are types of RNNs designed to remember information for an extended period. Moreover, the bidirectional LSTM/GRU keeps contextual information in both directions, which is helpful in text classification. RNNs have also been used to generate mathematical proofs and translate human thoughts into words. 

• Autoencoders are deep learning encoder-decoders that approximate a mapping from X to X, i.e., input=output. They first compress the input features into a lower-dimensional representation (sometimes called a latent code, latent vector, or latent representation) and learn to reconstruct the input. The representation vector can be used as input to a separate model, so this technique can be used for dimensionality reduction. Among specialists in many other fields, geneticists have applied autoencoders to spot mutations associated with diseases in amino acid sequences. 

• Encoder-decoder sequence-to-sequence : The encoder-decoder seq2seq architecture is an adaptation to autoencoders specialized for translation, summarization, and similar tasks. The encoder encapsulates the information in a text into an encoded vector. Unlike an autoencoder, instead of reconstructing the input from the encoded vector, the decoder’s task is to generate a different desired output, like a translation or summary. 

• Transformers : The transformer, a model architecture first described in the 2017 paper “ Attention Is All You Need ” (Vaswani, Shazeer, Parmar, et al.), forgoes recurrence and instead relies entirely on a self-attention mechanism to draw global dependencies between input and output. Since this mechanism processes all words at once (instead of one at a time) that decreases training speed and inference cost compared to RNNs, especially since it is parallelizable. The transformer architecture has revolutionized NLP in recent years, leading to models including BLOOM , Jurassic-X , and Turing-NLG . It has also been successfully applied to a variety of different vision tasks , including making 3D images .

Six Important Natural Language Processing (NLP) Models

Over the years, many NLP models have made waves within the AI community, and some have even made headlines in the mainstream news. The most famous of these have been chatbots and language models. Here are some of them:

• Eliza was developed in the mid-1960s to try to solve the Turing Test; that is, to fool people into thinking they’re conversing with another human being rather than a machine. Eliza used pattern matching and a series of rules without encoding the context of the language.
• Tay was a chatbot that Microsoft launched in 2016. It was supposed to tweet like a teen and learn from conversations with real users on Twitter. The bot adopted phrases from users who tweeted sexist and racist comments, and Microsoft deactivated it not long afterward. Tay illustrates some points made by the “Stochastic Parrots” paper, particularly the danger of not debiasing data.
• BERT and his Muppet friends: Many deep learning models for NLP are named after Muppet characters , including ELMo , BERT , Big BIRD , ERNIE , Kermit , Grover , RoBERTa , and Rosita . Most of these models are good at providing contextual embeddings and enhanced knowledge representation.
• Generative Pre-Trained Transformer 3 (GPT-3) is a 175 billion parameter model that can write original prose with human-equivalent fluency in response to an input prompt. The model is based on the transformer architecture. The previous version, GPT-2, is open source. Microsoft acquired an exclusive license to access GPT-3’s underlying model from its developer OpenAI, but other users can interact with it via an application programming interface (API). Several groups including EleutherAI and Meta have released open source interpretations of GPT-3. 
• Language Model for Dialogue Applications (LaMDA) is a conversational chatbot developed by Google. LaMDA is a transformer-based model trained on dialogue rather than the usual web text. The system aims to provide sensible and specific responses to conversations. Google developer Blake Lemoine came to believe that LaMDA is sentient. Lemoine had detailed conversations with AI about his rights and personhood. During one of these conversations, the AI changed Lemoine’s mind about Isaac Asimov’s third law of robotics. Lemoine claimed that LaMDA was sentient, but the idea was disputed by many observers and commentators. Subsequently, Google placed Lemoine on administrative leave for distributing proprietary information and ultimately fired him.
• Mixture of Experts ( MoE): While most deep learning models use the same set of parameters to process every input, MoE models aim to provide different parameters for different inputs based on efficient routing algorithms to achieve higher performance . Switch Transformer is an example of the MoE approach that aims to reduce communication and computational costs.

Programming Languages, Libraries, And Frameworks For Natural Language Processing (NLP)

Many languages and libraries support NLP. Here are a few of the most useful.

• Natural Language Toolkit (NLTK) is one of the first NLP libraries written in Python. It provides easy-to-use interfaces to corpora and lexical resources such as WordNet . It also provides a suite of text-processing libraries for classification, tagging, stemming, parsing, and semantic reasoning.
• spaCy is one of the most versatile open source NLP libraries. It supports more than 66 languages. spaCy also provides pre-trained word vectors and implements many popular models like BERT. spaCy can be used for building production-ready systems for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and so on.
• Deep Learning libraries: Popular deep learning libraries include TensorFlow and PyTorch , which make it easier to create models with features like automatic differentiation. These libraries are the most common tools for developing NLP models.
• Hugging Face offers open-source implementations and weights of over 135 state-of-the-art models. The repository enables easy customization and training of the models.
• Gensim provides vector space modeling and topic modeling algorithms.
• R : Many early NLP models were written in R, and R is still widely used by data scientists and statisticians. Libraries in R for NLP include TidyText , Weka , Word2Vec , SpaCyR , TensorFlow , and PyTorch .
• Many other languages including JavaScript, Java, and Julia have libraries that implement NLP methods.

Controversies Surrounding Natural Language Processing (NLP)

NLP has been at the center of a number of controversies. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world. 

• Stochastic parrots: A 2021 paper titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” by Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell examines how language models may repeat and amplify biases found in their training data. The authors point out that huge, uncurated datasets scraped from the web are bound to include social biases and other undesirable information, and models that are trained on them will absorb these flaws. They advocate greater care in curating and documenting datasets, evaluating a model’s potential impact prior to development, and encouraging research in directions other than designing ever-larger architectures to ingest ever-larger datasets.
• Coherence versus sentience: Recently, a Google engineer tasked with evaluating the LaMDA language model was so impressed by the quality of its chat output that he believed it to be sentient . The fallacy of attributing human-like intelligence to AI dates back to some of the earliest NLP experiments. 
• Environmental impact: Large language models require a lot of energy during both training and inference. One study estimated that training a single large language model can emit five times as much carbon dioxide as a single automobile over its operational lifespan. Another study found that models consume even more energy during inference than training. As for solutions, researchers have proposed using cloud servers located in countries with lots of renewable energy as one way to offset this impact. 
• High cost leaves out non-corporate researchers: The computational requirements needed to train or deploy large language models are too expensive for many small companies . Some experts worry that this could block many capable engineers from contributing to innovation in AI. 
• Black box: When a deep learning model renders an output, it’s difficult or impossible to know why it generated that particular result. While traditional models like logistic regression enable engineers to examine the impact on the output of individual features, neural network methods in natural language processing are essentially black boxes. Such systems are said to be “not explainable,” since we can’t explain how they arrived at their output. An effective approach to achieve explainability is especially important in areas like banking, where regulators want to confirm that a natural language processing system doesn’t discriminate against some groups of people, and law enforcement, where models trained on historical data may perpetuate historical biases against certain groups.

“ Nonsense on stilts ”: Writer Gary Marcus has criticized deep learning-based NLP for generating sophisticated language that misleads users to believe that natural language algorithms understand what they are saying and mistakenly assume they are capable of more sophisticated reasoning than is currently possible.

How To Get Started In Natural Language Processing (NLP)

If you are just starting out, many excellent courses can help.

If you want to learn more about NLP, try reading research papers. Work through the papers that introduced the models and techniques described in this article. Most are easy to find on arxiv.org . You might also take a look at these resources: 

• The Batch : A weekly newsletter that tells you what matters in AI. It’s the best way to keep up with developments in deep learning.
• NLP News : A newsletter from Sebastian Ruder, a research scientist at Google, focused on what’s new in NLP. 
• Papers with Code : A web repository of machine learning research, tasks, benchmarks, and datasets.

We highly recommend learning to implement basic algorithms (linear and logistic regression, Naive Bayes, decision trees, and vanilla neural networks) in Python. The next step is to take an open-source implementation and adapt it to a new dataset or task. 

NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud , determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. 

Aspiring NLP practitioners can begin by familiarizing themselves with foundational AI skills: performing basic mathematics, coding in Python, and using algorithms like decision trees, Naive Bayes, and logistic regression. Online courses can help you build your foundation. They can also help as you proceed into specialized topics. Specializing in NLP requires a working knowledge of things like neural networks, frameworks like PyTorch and TensorFlow, and various data preprocessing techniques. The transformer architecture, which has revolutionized the field since it was introduced in 2017, is an especially important architecture.

NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions.

This page is only a brief overview of what NLP is all about. If you have an appetite for more, DeepLearning.AI offers courses for everyone in their NLP journey, from AI beginners and those who are ready to specialize . No matter your current level of expertise or aspirations, remember to keep learning!

Natural Language Processing

Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.

Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.

Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.

On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.

Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.

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Towards Developing Uniform Lexicon Based Sorting Algorithm for Three Prominent Indo-Aryan Languages

Three different Indic/Indo-Aryan languages - Bengali, Hindi and Nepali have been explored here in character level to find out similarities and dissimilarities. Having shared the same root, the Sanskrit, Indic languages bear common characteristics. That is why computer and language scientists can take the opportunity to develop common Natural Language Processing (NLP) techniques or algorithms. Bearing the concept in mind, we compare and analyze these three languages character by character. As an application of the hypothesis, we also developed a uniform sorting algorithm in two steps, first for the Bengali and Nepali languages only and then extended it for Hindi in the second step. Our thorough investigation with more than 30,000 words from each language suggests that, the algorithm maintains total accuracy as set by the local language authorities of the respective languages and good efficiency.

Efficient Channel Attention Based Encoder–Decoder Approach for Image Captioning in Hindi

Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language modeling, respectively. In the existing literature, most of the works have been carried out for image captioning in the English language. This article presents a novel method for image captioning in the Hindi language using encoder–decoder based deep learning architecture with efficient channel attention. The key contribution of this work is the deployment of an efficient channel attention mechanism with bahdanau attention and a gated recurrent unit for developing an image captioning model in the Hindi language. Color images usually consist of three channels, namely red, green, and blue. The channel attention mechanism focuses on an image’s important channel while performing the convolution, which is basically to assign higher importance to specific channels over others. The channel attention mechanism has been shown to have great potential for improving the efficiency of deep convolution neural networks (CNNs). The proposed encoder–decoder architecture utilizes the recently introduced ECA-NET CNN to integrate the channel attention mechanism. Hindi is the fourth most spoken language globally, widely spoken in India and South Asia; it is India’s official language. By translating the well-known MSCOCO dataset from English to Hindi, a dataset for image captioning in Hindi is manually created. The efficiency of the proposed method is compared with other baselines in terms of Bilingual Evaluation Understudy (BLEU) scores, and the results obtained illustrate that the method proposed outperforms other baselines. The proposed method has attained improvements of 0.59%, 2.51%, 4.38%, and 3.30% in terms of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores, respectively, with respect to the state-of-the-art. Qualities of the generated captions are further assessed manually in terms of adequacy and fluency to illustrate the proposed method’s efficacy.

Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

A Computational Look at Oral History Archives

Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

Which environmental features contribute to positive and negative perceptions of urban parks? A cross-cultural comparison using online reviews and Natural Language Processing methods

Natural language processing for smart construction: current status and future directions, attention-based unsupervised keyphrase extraction and phrase graph for covid-19 medical literature retrieval.

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks—BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB .

An ensemble approach for healthcare application and diagnosis using natural language processing

Machine learning and natural language processing enable a data-oriented experimental design approach for producing biochar and hydrochar from biomass, export citation format, share document.

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Perspectives for Natural Language Processing between AI, Linguistics and Cognitive Science

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Natural Language Processing (NLP) today - like most of Artificial Intelligence (AI) - is much more of an "engineering" discipline than it originally was, when it sought to develop a general theory of human language understanding that not only translates into language technology, but that is also

Natural Language Processing (NLP) today - like most of Artificial Intelligence (AI) - is much more of an "engineering" discipline than it originally was, when it sought to develop a general theory of human language understanding that not only translates into language technology, but that is also linguistically meaningful and cognitively plausible. At first glance, this trend seems to be clearly connected to recent rapid development. Such development, in the last ten years, was driven to a large extent by the adoption of deep learning techniques. However, it can be argued that the move towards deep learning has the potential of bringing NLP back to its roots after all. Some recent activities in this direction include: ● Techniques like multi-task learning have been used to integrate cognitive data as supervision in NLP tasks 1 ; ● Pre-training/fine-tuning regimens are potentially interpretable in terms of cognitive mechanisms like general competencies applied to specific tasks 2 ; ● The ability of modern models for 'few-shot' or even 'zero-shot' performance on novel tasks mirrors human performance 3 ; ● Analysis of complex neural network architectures like transformer models has found evidence of unsupervised structure learning that mirrors classical linguistic structures using so-called 'probing studies' 4,5 . The last generation of neural network architectures has allowed AI and NLP to make unprecedented progress in developing systems endowed with natural language capabilities. Such systems (e.g., GPT) are typically trained with huge computational infrastructures on large amounts of textual data from which they acquire knowledge thanks to their extraordinary ability to record and generalize the statistical patterns found in data. However, the debate about the human-like semantic abilities that such “juggernaut models” really acquire is still wide open. In fact, despite the figures typically reported to show the success of AI on various benchmarks, other research argues that their semantic competence is still very brittle 6,7,8 . Thus, an important limitation of current AI research is the lack of attention to the mechanisms behind human language understanding. The latter does not only consist in a brute-force, data-intensive processing of statistical regularities, but it is also governed by complex inferential mechanisms that integrate linguistic information and contextual knowledge coming from different sources and potentially different modalities ("grounding"). We posit that the possibility for new breakthroughs in the study of human and machine intelligence calls for a new alliance between NLP, AI, linguistic and cognitive research. The current computational paradigms can offer new ways to explore human language learning and processing, while linguistic and cognitive research can contribute by highlighting those aspects of human intelligence that systems need to model or incorporate within their architectures. The current Research Topic aims at fostering this process by discussing perspectives forward for NLP, given the data and learning devices we have at hand and given the conflicting interests of the participating fields. Suitable topics include, but are not limited to: ● What can NLP do for linguistics, and vice versa? ● What can NLP do for cognitive science, and vice versa? ● How does modeling language relate to modeling general intelligence? ● How do we measure short-term long-term success in NLP? ● Is interdisciplinary research the way ahead for NLP? What are hallmarks for successful interdisciplinary research on language? We invite not only empirical work but also theoretical (methodological) considerations and position papers. Information for the authors: - To ensure a quick and high-quality reviewing process, we invite authors to act as reviewers for other submissions to the collection. - We encourage authors to submit an abstract by June 15th to allow the Guest Editors to assess the relevance of the paper to the collection. References: 1. M. Barrett, and A. Søgaard. "Reading behavior predicts syntactic categories." Proceedings of the 19th conference on Computational Natural Language Learning. 2015. 2. T. Flesch, et al. "Comparing continual task learning in minds and machines." Proceedings of the National Academy of Sciences. 2018. 3. A. Lazaridou, et al. "Hubness and pollution: Delving into cross-space mapping for zero-shot learning." Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015. 4. J. Hewitt, and C. D. Manning. "A structural probe for finding syntax in word representations." Proceedings of the Annual Meeting of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019. 5. I. Tenney, et al. "BERT Rediscovers the Classical NLP Pipeline." Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. 6. B. M. Lake, and M. Baroni. "Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks”. Proceedings of the 35th International Conference on Machine Learning. 2018. 7. A. Ravichander, et al. "Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?." arXiv:2005.00719. 2020. 8. E. M. Bender, and A. Koller. "Climbing towards NLU: On meaning, form, and understanding in the age of data." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Keywords : Natural Language Processing, Linguistics, Cognitive Science, Human Language Understanding, Language Technology, Deep Learning, Multi-task Learning

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Natural Language Processing (NLP) Projects & Topics For Beginners [2023]

What are Natural Language Processing Projects?

NLP project ideas advanced encompass various applications and research areas that leverage computational techniques to understand, manipulate, and generate human language. These projects harness the power of artificial intelligence and machine learning to process and analyze textual data in ways that mimic human understanding and communication. Here are some key aspects and examples of NLP projects:

1. Text Classification

NLP can be used to classify text documents into predefined categories automatically. This is useful in sentiment analysis, spam detection, and topic categorization. For instance, classifying customer reviews as positive or negative to gauge product sentiment.

It also plays a crucial role in topic categorization, aiding in the organization and understanding of large volumes of textual data. Natural Language Processing projects help us understand Text Classification better by letting us put theories into action. 

Through hands-on projects, we get to apply text classification algorithms to real situations, like figuring out if customer reviews are positive or negative. These projects expose us to different types of text data challenges, such as messy information or imbalanced categories, helping us learn how to handle them.

2. Named Entity Recognition (NER)

Named Entity Recognition (NER) is a vital part of Natural Language Processing (NLP) that helps machines identify and categorize specific entities in a given text.

NLP models can identify and categorize entities such as names of people, organizations, locations, and dates within text. This is crucial for information extraction tasks like news article analysis or document summarization.

Natural Language Processing projects focusing on Named Entity Recognition provide hands-on experience with extracting valuable information from unstructured text. For instance, when analyzing news articles, NER can be applied to pinpoint key entities, making it easier to understand the main players, locations, and dates involved in a story.

Projects in NLP also allow practitioners to explore practical applications of NER beyond its standalone use. For instance, integrating NER into larger projects like document summarization or information extraction showcases its versatility and relevance in solving complex NLP challenges.

3. Machine Translation

Projects in this domain focus on developing algorithms that translate text from one language to another. Prominent examples include Google Translate and neural machine translation models.

The goal of machine translation is to enable seamless communication between people who speak different languages, breaking down language barriers and fostering global understanding. MT systems require extensive training data in multiple languages to learn the patterns and nuances of language pairs. Projects in NLP involve sourcing and preprocessing large bilingual corpora, including translated texts, to train robust translation models.

Natural Language Processing projects in machine translation provide a practical understanding of the technical, linguistic, and ethical dimensions involved in building effective translation models, contributing to the ongoing efforts to facilitate cross-language communication in diverse contexts.

4. Text Generation

Text generation is a fascinating aspect of Natural Language Processing (NLP) that involves creating coherent and contextually relevant text automatically using computer algorithms. These algorithms can range from traditional rule-based methods to more advanced deep learning models.

NLP models like GPT-3 can generate human-like text, making them useful for content generation, chatbots, and creative writing applications.

The goal of text generation is to produce human-like text that follows the style and structure of a given language. In NLP based projects , text generation often explores conditional scenarios, where the output is influenced by specific input conditions, making it applicable for tasks like chatbot responses or context-based sentence completion. Data preprocessing plays a pivotal role in preparing diverse and representative datasets for effective model training. 

5. Question-Answering Systems

Question-Answering (QA) Systems represent a significant area within Natural Language Processing that focuses on developing algorithms capable of comprehending questions posed in natural language and providing relevant and accurate answers. These systems aim to bridge the gap between human language understanding and machine processing, allowing users to interact with computers in a more conversational manner.

These nlp project ideas involve building systems that can understand questions posed in natural language and provide relevant answers. IBM’s Watson is a well-known example.

NLP project ideas based on QA systems may also explore context-aware systems, where the model considers the broader context of a conversation or passage to provide more accurate answers.

6. Speech Recognition

While technically part of the broader field of speech processing, NLP techniques are used in transcribing spoken language into written text, as seen in applications like voice assistants (e.g., Siri and Alexa).

These NLP related projects involve the collection of high-quality audio datasets with diverse speakers and linguistic variations that are essential for training robust models. Preprocessing steps involve converting audio signals into a format suitable for analysis, often using techniques like spectrogram representations.

NLP projects in Python have diverse applications especially when it comes to speech recognition. They range from the development of voice assistants and dictation software to transcription services and voice-controlled devices. The outcomes contribute significantly to the creation of hands-free interfaces, facilitating accessibility features for differently-abled individuals, and propelling advancements in voice-activated technologies.

All in all, there are many easy NLP projects in Speech Recognition that beginners can take up to develop a deeper understanding of spoken language by computers, enhancing human-computer interaction intuitively and expanding accessibility across various applications and user scenarios.

7. Text Summarization

NLP can automatically generate concise summaries of lengthy texts, making it easier to digest information from news articles, research papers, or legal documents.

NLP based projects in Text Summarization explore different techniques, such as extractive summarization, where the algorithm selects and combines existing sentences, and abstractive summarization, where it generates new sentences to convey the essential meaning.

The applications of Text Summarization projects are diverse and impactful. They are used to quickly condense lengthy articles, news, or documents, providing readers with a concise version that captures the main ideas. These projects in NLP essentially empower computers to act as efficient summarizers, making information more accessible and saving time for users who need a quick understanding of complex texts.

8. Sentiment Analysis

Analyzing social media data and customer reviews to determine public sentiment toward products, services, or political issues is a common NLP application.

NLP project ideas focusing on Sentiment Analysis, algorithms are trained to analyze words and phrases to determine the overall sentiment conveyed by a piece of text.

These projects are particularly useful in various applications, such as assessing customer reviews, monitoring social media sentiments, or gauging public opinion. The goal is to help businesses and organizations understand how people feel about their products, services, or specific topics.

9. Language Modeling

Language Modeling is a fundamental concept in Natural Language Processing (NLP) that involves teaching computers to understand and predict the structure and patterns of human language

Creating and fine-tuning language models, such as BERT and GPT, for various downstream tasks forms the core of many NLP projects. These models learn to represent and understand language in a generalized manner.

Language Modeling projects in NLP play a pivotal role in enabling computers to grasp the intricacies of human language, facilitating applications that require language understanding and generation. These projects are essential in various NLP applications, such as speech recognition, machine translation, and text generation. By understanding the structure of language, computers can generate coherent and contextually relevant text, making interactions with machines more natural and human-like.

What are the Different Best Platforms to Work on Natural Language Processing Projects?

Here are some of the best platforms for nlp projects for final year :

1. Python and Libraries

Python is the most popular programming language for NLP due to its extensive libraries and frameworks. Its user-friendly syntax and readability also make it particularly suitable for students with varying programming experience. Therefore, it stands out as an excellent platform to undertake NLP projects for final year students.

Libraries like NLTK, spaCy, gensim, and the Transformers library by Hugging Face provide essential NLP functionalities and pre-trained models. In addition, visualization tools like Matplotlib and Seaborn contribute to effective project presentation. Collectively, the combination of Python and its libraries provides a conducive and resource-rich environment for successful Natural Language Processing with Python projects.

2. TensorFlow and PyTorch

These deep learning frameworks provide powerful tools for building and training neural network models, including NLP models. Researchers and developers can choose between them based on their preferences.

They are powerful tools to aid in building smart computer systems, especially for final year students working on NLP related projects. TensorFlow, made by Google, is known for being flexible and great for big projects on machine learning and deep learning. On the other hand, PyTorch’s dynamic graph is well-suited for research-oriented work. Both frameworks have rich documentation, and active communities, and support a variety of model architectures.

3. Google Colab

For cloud-based NLP development, Google Colab offers free access to GPU and TPU resources, making it an excellent choice for training large NLP models without needing high-end hardware.

It serves more like a cloud-based platform, offering free access to GPUs and TPUs. It’s akin to a virtual workspace where users can run code, train models, and analyze data without the constraints of computational resources. Its integration with popular libraries like TensorFlow and PyTorch makes it an excellent choice for collaborative and resource-intensive Natural Language Processing projects.

SpaCy is a fast and efficient NLP library that excels at various NLP tasks, including tokenization, named entity recognition, and part-of-speech tagging. It also offers pre-trained models for multiple languages. SpaCy functions as a language expert in projects involving extensive text data. Its reputation for speed and efficiency makes it a preferred tool for NLP projects for beginners.

Docker containers can create reproducible and portable NLP environments, ensuring consistency across development and deployment stages.

It acts as a versatile containerization tool, allowing users to package an entire project, along with its dependencies, into a single, reproducible unit. This is particularly advantageous for NLP projects with specific software configurations, ensuring consistency across different environments. Docker addresses the common challenge of project reproducibility.

6. AWS, Azure, and Google Cloud

These cloud platforms offer scalable compute resources and specialized NLP services like Amazon Comprehend, Azure Text Analytics, and Google Cloud NLP, simplifying the deployment of NLP solutions at scale.

These platforms are like powerful virtual data centers offering computing power services to storage and machine learning tools. AWS is known for its extensive service offerings, Azure seamlessly integrates with Microsoft technologies, and Google Cloud excels in data analytics and machine learning. For students taking up both NLP mini project topics and big project topics, these platforms provide access to cutting-edge technologies without the need for substantial hardware investments.

Kaggle provides datasets, competitions, and a collaborative platform for NLP practitioners to share code and insights. It’s a great resource for learning and benchmarking NLP models.

Like a virtual playground for data scientists, Kaggle provides datasets for analysis, hosts machine learning competitions, and allows users to create and share code through Jupyter notebooks. For students working on NLP projects, it is a collaborative space where they can apply their data science skills in real-world scenarios, learn from others, and build a portfolio that demonstrates their capabilities to potential employers.

GitHub is a repository for NLP project code, facilitating collaboration and version control. Many NLP libraries and models are open-source and hosted on GitHub.

Students can host their code repositories on GitHub, track changes, and collaborate with peers.

It’s an invaluable tool for final-year projects, facilitating version management, issue tracking, and showcasing their works in natural language processing projects for GitHub to prospective employers.

9. Apache Spark

Apache Spark can be used for handling large-scale NLP tasks for distributed data processing and machine learning. Apache Spark is an open-source framework for big data processing, handling tasks like batch processing, streaming, machine learning, and graph processing efficiently. With its in-memory processing and support for multiple languages, it’s a versatile tool for final-year projects dealing with large datasets or complex computations, making tasks scalable and faster.

NLP Projects & Topics

Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP based projects that match your learning needs. So, we have collated some examples to get you started. So, if you are a ML beginner, the best thing you can do is work on some NLP projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting NLP projects which beginners can work on to put their knowledge to test. In this article, you will find top NLP project ideas for beginners to get hands-on experience on NLP.

But first, let’s address the more pertinent question that must be lurking in your mind: why to build NLP projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.

NLP is all about analyzing and representing human language computationally. It equips computers to respond using context clues just like a human would. Some everyday applications of NLP around us include spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, etc. As you start working on NLP projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

In the last few years, NLP has garnered considerable attention across industries. And the rise of technologies like text and speech recognition, sentiment analysis, and machine-to-human communications, has inspired several innovations. Research suggests that the global NLP market will hit US$ 28.6 billion in market value in 2026. 

When it comes to building real-life applications, knowledge of machine learning basics is crucial. However, it is not essential to have an intensive background in mathematics or theoretical computer science. With a project-based approach, you can develop and train your models even without technical credentials. Learn more about NLP Applications.

To help you in this journey, we have compiled a list of NLP project ideas , which are inspired by actual software products sold by companies. You can use these resources to brush up your ML fundamentals, understand their applications, and pick up new skills during the implementation stage. The more you experiment with different NLP projects, the more knowledge you gain.

Before we dive into our lineup of NLP projects , let us first note the explanatory structure. 

The project implementation plan

All the nlp projects for final year included in this article will have a similar architecture, which is given below:

• Implementing a pre-trained model
• Deploying the model as an API
• Connecting the API to your main application

This pattern is known as real-time inference and brings in multiple benefits to your NLP design. Firstly, it offloads your main application to a server that is built explicitly for ML models. So, it makes the computation process less cumbersome. Next, it lets you incorporate predictions via an API. And finally, it enables you to deploy the APIs and automate the entire infrastructure by using open-source tools, such as Cortex. 

Here is a summary of how you can deploy machine learning models with Cortex:

• Write a Python script to serve up predictions.
• Write a configuration file to define your deployment.
• Run ‘cortex deploys’ from your command line.

Now that we have given you the outline let us move on to our list! 

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So, here are a few NLP Projects which beginners can work on:

NLP Project Ideas

This list of NLP projects for students is suited for beginners, intermediates & experts. These NLP projects will get you going with all the practicalities you need to succeed in your career.

Further, if you’re looking for NLP based projects for final year, this list should get you going. So, without further ado, let’s jump straight into some NLP projects that will strengthen your base and allow you to climb up the ladder. This list is also great for Natural Language Processing projects in Python . 

Here are some NLP project idea that should help you take a step forward in the right direction.

1. A customer support bot

One of the best ideas to start experimenting you hands-on projects on nlp for students is working on customer support bot. A conventional chatbot answers basic customer queries and routine requests with canned responses. But these bots cannot recognize more nuanced questions. So, support bots are now equipped with artificial intelligence and machine learning technologies to overcome these limitations. In addition to understanding and comparing user inputs, they can generate answers to questions on their own without pre-written responses. 

For example, Reply.ai has built a custom ML-powered bot to provide customer support. According to the company, an average organization can take care of almost 40 % of its inbound support requests with their tool. Now, let us describe the model required to implement a project inspired by this product. 

You can use Microsoft’s DialoGPT, which is a pre-trained dialogue response generation model. It extends the systems of PyTorch Transformers (from Hugging Face) and GPT-2 (from OpenAI) to return answers to the text queries entered. You can run an entire DialoGPT deployment with Cortex. There are several repositories available online for you to clone. Once you have deployed the API, connect it to your front-end UI, and enhance your customer service efficiency!

Read:  How to make chatbot in Python?

2. A language identifier

Have you noticed that Google Chrome can detect which language in which a web page is written? It can do so by using a language identifier based on a neural network model. 

This is an excellent nlp project in python for beginners. The process of determining the language of a particular body of text involves rummaging through different dialects, slangs, common words between different languages, and the use of multiple languages in one page. But with machine learning, this task becomes a lot simpler.

You can construct your own language identifier with the fastText model by Facebook. The model is an extension of the word2vec tool and uses word embeddings to understand a language. Here, word vectors allow you to map a word based on its semantics — for instance, upon subtracting the vector for “male” from the vector for “king” and adding the vector for “female,” you will end up with the vector for “queen.”

A distinctive characteristic of fastText is that it can understand obscure words by breaking them down into n-grams. When it is given an unfamiliar word, it analyzes the smaller n-grams, or the familiar roots present within it to find the meaning. Deploying fastTExt as an API is quite straightforward, especially when you can take help from online repositories.

3. An ML-powered autocomplete feature

Autocomplete typically functions via the key value lookup, wherein the incomplete terms entered by the user are compared to a dictionary to suggest possible options of words. This feature can be taken up a notch with machine learning by predicting the next words or phrases in your message.

Here, the model will be trained on user inputs instead of referencing a static dictionary. A prime example of an ML-based autocomplete is Gmail’s ‘Smart Reply’ option, which generates relevant replies to your emails. Now, let us see how you can build such a feature. 

For this advanced nlp projects , you can use the RoBERTa language model. It was introduced at Facebook by improving Google’s BERT technique. Its training methodology and computing power outperform other models in many NLP metrics.

To receive your prediction using this model, you would first need to load a pre-trained RoBERTa through PyTorch Hub. Then, use the built-in method of fill_mask(), which would let you pass in a string and guide your direction to where RoBERTa would predict the next word or phrase. After this, you can deploy RoBERTa as an API and write a front-end function to query your model with user input. Mentioning NLP projects can help your resume look much more interesting than others.

4. A predictive text generator

This is one of the interesting NLP projects. Have you ever heard of the game AI Dungeon 2? It is a classic example of a text adventure game built using the GPT-2 prediction model. The game is trained on an archive of interactive fiction and demonstrates the wonders of auto-generated text by coming up with open-ended storylines. Although machine learning in the area of game development is still at a nascent stage, it is set to transform experiences in the near future. Learn how python performs in game development .

DeepTabNine serves as another example of auto-generated text. It is an ML-powered coding autocomplete for a variety of programming languages. You can install it as an add-on to use within your IDE and benefit from fast and accurate code suggestions. Let us see how you can create your own version of this NLP tool. 

You should go for Open AI’s GPT-2 model for this project. It is particularly easy to implement a full pre-trained model and to interact with it thereafter. You can refer to online tutorials to deploy it using the Cortex platform. And this is the perfect idea for your next NLP project!

Read: Machine Learning Project Ideas

5. A media monitor

One of the best ideas to start experimenting you hands-on NLP projects for students is working on media monitor. In the modern business environment, user opinion is a crucial denominator of your brand’s success. Customers can openly share how they feel about your products on social media and other digital platforms. Therefore, today’s businesses want to track online mentions of their brand. The most significant fillip to these monitoring efforts has come from the use of machine learning. 

For example, the analytics platform Keyhole can filter all the posts in your social media stream and provide you with a sentiment timeline that displays the positive, neutral, or negative opinion. Similarly, an ML-backed sift through news sites. Take the case of the financial sector where organizations can apply NLP to gauge the sentiment about their company from digital news sources. 

Such media analytics can also improve customer service. For example, providers of financial services can monitor and gain insights from relevant news events (such as oil spills) to assist clients who have holdings in that industry. 

You can follow these steps to execute a project on this topic: 

• Use the SequenceTagger framework from the Flair library. (Flair is an open-source repository built on PyTorch that excels in dealing with Named Entity Recognition problems.)
• Use Cortex’s Predictor API to implement Flair.

We are currently experiencing an exponential increase in data from the internet, personal devices, and social media. And with the rising business need for harnessing value from this largely unstructured data, the use of NLP instruments will dominate the industry in the coming years.

Such developments will also jumpstart the momentum for innovations and breakthroughs, which will impact not only the big players but also influence small businesses to introduce workarounds. 

Also read: AI Project Ideas and Topics for Beginners

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Natural language processing techniques to use in python.

Making computers read unorganized texts and extract useful information from them is the aim of natural language processing (NLP). Many NLP approaches can be implemented using a few lines of Python code, courtesy of accessible libraries like NLTK, and spaCy. These approaches can also work great as NLP topics for presentation . 

Here are some techniques of Natural Language Processing projects in Python – 

• Named Entity Recognition or NER – A technique called named entity recognition is used to find and categorise named entities in text into groups like people, organisations, places, expressions of times, amounts, percentages, etc. It is used to improve content classification, customer service, recommendation systems, and search engine algorithms, among other things.
• Analysis of Sentiment – One of the most well-known NLP approaches, sentiment analysis examines text (such as comments, reviews, or documents) to identify whether the information is good, poor, or indifferent. Numerous industries, including banking, healthcare, and customer service, can use it.
• BoW or Bag of Words – A format that transforms text into stationary variables is called the Bag of Words (BoW) model. This makes it easier for us to convert text to numbers to be used in machine learning. The model is simply interested in the number of terms in the text and isn’t focused on word order. It may be used for document categorisation, information retrieval, and NLP. Cleaning raw text, tokenisation, constructing a vocabulary, and creating vectors are all steps in the normal BoW approach.
• TF-IDF (Term Frequency – Inverse Document Frequency) – The TF-IDF calculates “weights” that describe how significant a word is in the document.  The quantity of documents that include a term reduces the TF-IDF value, which rises according to the frequency of its use in the document. Simply said, the phrase is rare, more distinctive, or more important the higher the TF-IDF score, and vice versa. It has uses in information retrieval, similar to how browsers try to yield results that are most pertinent to your request. 

TF and IDF are calculated in different ways. 

TF = (Number of duplicate words in a document) / (Number of words in a document)

IDF = Log {(Number of documents) / (Number of documents with the word)}

• Wordcloud – A common method for locating keywords in a document is word clouds. In a Wordcloud, words that are used more frequently have larger, stronger fonts, while those that are used less frequently have smaller, thinner fonts. With the ‘Wordcloud’ library and the ‘stylecloud’ module, you can create simplistic Wordclouds in Python. This makes NLP projects in Python very successful. 

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Nlp research topics –  .

To ace NLP projects in Python , it is necessary to conduct thorough research. Here are some NLP research topics that will help you in your thesis and also work great as NLP topics for presentation – 

• Biomedical Text Mining
• Computer Vision and also NLP
• Deep Linguistic Processing
• Controlled Natural Language
• Language Resources and also Architectures for NLP
• Sentiment Analysis and also Opinion Mining
• NLP includes Artificial Intelligence
• Issues includes Natural language understanding and also Creation
• Extraction of Actionable Intelligence also from Social Media
• Efficient Information also Extraction Techniques
• Use of Rule also based Approach or Statistical Approach
• Topic Modelling in Web data

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In this article, we covered some NLP projects that will help you implement ML models with rudimentary knowledge software development. We also discussed the real-world applicability and functionality of these products. So, use these topics as reference points to hone your practical skills and propel your career and business forward! 

Only by working with tools and practise can you understand how infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our NLP projects guide to build your very own NLP projects!

If you wish to improve your NLP skills, you need to get your hands on these NLP projects. If you’re interested to learn more about machine learning online course , check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

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Frequently Asked Questions (FAQs)

These projects are very basic, someone with a good knowledge of NLP can easily manage to pick and finish any of these projects.

Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.

Natural language processing (NLP) is a subject of computer science—specifically, a branch of artificial intelligence (AI)—concerning the ability of computers to comprehend text and spoken words in the same manner that humans can. Computational linguistics—rule-based human language modeling—is combined with statistical, learning algorithms, and deep learning models.

The design of all the projects will be the same: Implementing a pre-trained model, deploying the model as an API, and connecting the API to your primary application. Real-time inference is a pattern that delivers several benefits to your NLP design. To begin with, it offloads your core application to a server designed specifically for machine learning models. As a result, the computation procedure is simplified. Then, using an API, you may incorporate predictions. Finally, it allows you to use open-source tools like Cortex to install APIs and automate the entire architecture.

This is a fantastic NLP project for newcomers. The method of identifying the language of a body of text entails combing through many dialects, slangs, cross-language common terms, and the use of numerous languages on a single page. This task, however, becomes a lot easier with machine learning. With Facebook's fastText concept, you can create your own language identifier. The model employs word embeddings to comprehend a language and is an expansion of the word2vec tool. Word vectors enable you to map a word based on its semantics — for example, you can get the vector for Queen by subtracting the vector for Male from the vector for King and adding the vector for Female.

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1. INTRODUCTION

2. data and preprocessing, 3. performed experiments, applied methods and analysis of results, 4. concluding remarks, the state of the art of natural language processing—a systematic automated review of nlp literature using nlp techniques.

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Jan Sawicki , Maria Ganzha , Marcin Paprzycki; The State of the Art of Natural Language Processing—A Systematic Automated Review of NLP Literature Using NLP Techniques. Data Intelligence 2023; 5 (3): 707–749. doi: https://doi.org/10.1162/dint_a_00213

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Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current “state of the field” and thus, to enter it. This brought the id-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

Natural language processing (NLP) is rapidly growing in popularity in a variety of domains, from closely related, like semantics [ 1 , 2 ] and linguistics [ 3 , 4 ] (e.g. inflection [ 5 ], phonetics and onomastics [ 6 ], automatic text correction [ 7 ]), named entity recognition [ 8 , 9 ] to distant ones, like biobliometry [ 10 ], cybersecurity [ 11 ], quantum mechanics [ 12 , 13 ], gender studies [ 14 , 15 ], chemistry [ 16 ] or orthodontia [ 17 ]. This, among others, brings an opportunity, for early-stage researchers, to enter the area. Since NLP can be applied to many domains and languages, and involves use of many techniques and approaches, it is important to realize where to start.

This contribution attempts at addressing this issue, by applying NLP techniques to analysis of NLP-focused literature. As a result, with a fully automated, systematic, visualization-driven literature analysis, a guide to the state-of-the-art of natural language processing is presented. In this way, two goals are achieved. (1) Providing introduction to NLP for scientists entering the field, and (2) supporting possible knowledge update for experienced researchers. The main research questions (RQs) considered in this work are:

RQ1: What datasets are considered to be most useful?

RQ2: Which languages, other than English, appear in NLP research?

RQ3: What are the most popular fields and topics in current NLP research?

Rq4: what particular tasks and problems are most often studied, rq5: is the field “homogenous”, or are there easily identifiable “subgroups”, rq6: how difficult is it to comprehend the nlp literature.

Taking into account that the proposed approach is, itself, anchored in NLP, this work is also an illustration of how selected standard NLP techniques can be used in practice, and which of them should be used for which purpose. However, it should be made clear that considerations presented in what follows should be treated as “illustrative examples”, not “strict guidelines”. Moreover, it should be stressed that none of the applied techniques has been optimized to the task (e.g. no hyperparameter tuning has been applied). This is a deliberate choice, as the goal is to provide an overview and “general ideas”, rather than overwhelm the reader with technical details of individual NLP approaches. For technical details, concerning optimization of mentioned approaches, reader should consult referenced literature.

The whole analysis has been performed in Python—a programming language which is ubiquitous in data science research and projects for years [ 18 , 19 , 20 , 21 , 22 , 23 ]. Python was also chosen for the following reasons:

It provides a heterogeneous environment

It allows use of Jupyter Notebooks ① , which allow quick and easy prototyping, testing and code sharing

There exists an abundance of data science libraries ② , which allow everything from acquiring the dataset, to visualizing the result

It offers readability and speed in development [ 24 ]

Presented analysis follows the order of research questions. To make the text more readable, readers are introduced to pertinent NLP methods in the context of answering individual questions.

At the beginning of NLP research, there is always data. This section introduces the dataset consisting of research papers used in this work, and describes how it was preprocessed.

2.1 Data Used in the Research

To adequately represent the domain, and to apply NLP techniques, it is necessary to select an abundant, and well-documented, repository of related texts (stored in a digital format). Moreover, to automatize the conducted analysis, and to allow easy reproduction, it is crucial to choose a set of papers, which can be easily accessed, e.g. a database with a functional Application Programming Interface (API). Finally, for obvious reasons, open access datasets are the natural targets for NLP-oriented work.

In the context of this work, while there are multiple repositories, which contain NLP-related literature, the best choice turned out to be arXiv (for the papers themselves, and for the metadata it provided), combined with the Semantic Scholar (for the “citation network” and other important metadata; see Section 3.3.1).

Note that other datasets have been considered, but were not selected. Reasons for this decision have been summarized in Table 1 .

2.1.1 Dataset Downloading and Filtering

The papers were fetched from arXiv on 26 August 2021. The resulting dataset includes all articles, which have been extracted as a result of issuing the query “natural language processing” ④ . As a result, 4712 articles were retrieved. Two articles were discarded because their PDFs were too complicated for the tools that were used for the text extraction (1 710.10229v1—problems with chart on page 15; 1803.07136v1 — problems with chart on page 6; see, also, section 2.2). Even though the query was not bounded by the “time when the article was uploaded to arXiv” parameter, it turned out that a solid majority of the articles had submission dates from the last decade. Specifically, the distribution was as follows:

192 records uploaded before 2010-01-01

243 records from between (including) 2010-01-01 and 2014-12-31

697 records from between (including) 2015-01-01 and 2017-12-31

3580 records uploaded after 2018-01-01

On the basis of this distribution, it was decided that there is no reason to impose time constraints, because the “old” works should not be able to “overshadow” the “newest” literature. Moreover, it was decided that it is worth keeping all available publications, as they might result in additional findings (e.g., as what concerns the most original work, described in Section 3.7.4).

Finally, all articles not written in English were discarded, reducing the total count to 4576 texts. This decision, while somewhat controversial, was made to be able to understand the results (by the authors of this contribution) and to avoid complex issues related to text translation. However, it is easy to observe that the number of texts not written in English (and stored in arXiv) was relatively small (< 5%). Nevertheless, this leaves open a question: what is the relationship between NLP-related work that is written in English and that written in other languages. However, addressing this topic is out of scope of this contribution.

2.2 Text Preprocessing

Obviously, the key information about a research contribution is contained in its text. Therefore, subsequent analysis applied NLP techniques to texts of downloaded papers. To do this, the following preprocessing has been applied. The PDFs have been converted to plain text, using pdfminer.six (a Python library ⑤ ). Here, notice that there are several other libraries that can also be used to convert PDF to text. Specifically, the following libraries have been tried: pdfminer ⑥ , pdftotree ⑦ , BeautifulSoup ⑧ . On the basis of performed tests, pdfminer.six was selected, because it provided the simplest API, produced results, which did not have to be further converted (as opposite to, e.g., BeautifulSoup), and performed the fastest conversion.

Use of different text analysis methods may require different preprocessing. Some methods, like keyphrase search, work best when the text is “thoroughly cleaned”; i.e. almost reduced to a “bag of words” [ 28 ]. This means that, for instance, words are lemmatized, there is no punctuation, etc. However, some more recent techniques (like text embeddings [ 29 ]) can (and should) be trained on a “dirty” text, like Wikipedia [ 30 ] dumps ⑨ or Common Crawl ⑩ . Hence, it is necessary to distinguish between (at least) two levels of text cleaning: (A) “delicately cleaned” text (in what follows, called “Stage 1” cleaning), where only parts insignificant to the NLP analysis are removed, and (B) a “very strictly cleaned” text (called “Stage 2” cleaning). Specifically, “Stage 1” cleaning includes removal of:

charts and diagrams improperly converted to text,

arXiv “watermarks”,

references section (which were not needed, since metadata from Semantic Scholar was used),

links, formulas, misconverted characters (e.g. “ff”).

Stage 2 cleaning is applied to the results of Stage 1 cleaning, and consists of the following operations:

All punctuation, numbers and other non-letter characters were removed, leaving only letters.

Adposition, adverb, conjunction, coordinating conjunction, determiner, interjection, numeral, particle, pronoun, punctuation, subordinating conjunction, symbol, end of line, space were removed. Parts of speech left after filtering were: verbs, nouns, auxiliaries and “other”. The “other” category is usually tagged for meaningless text, e.g. “asdfgh”. However, these were not deleted in case the algorithm detected something that was, in fact, important, e.g. domain-specific shortcuts and abbreviations like CNN, RNN, etc.

Words have been lemmatized.

Note that while individual NLP techniques may require more specific data cleaning, the two (Stage 1 and Stage 2) workflows are generic enough to be successfully applied in the majority of typical NLP applications.

This section traverses research questions RQ1 to RQ6 and summarizes the findings for each one of them. Furthermore, it introduces specific NLP methods used to address each question. Interested readers are invited to study referenced literature to find additional details.

3.1 RQ1: Finding Most Popular Datasets Used in NLP

As noted, a fundamental aspect for all data science projects is the data. Hence, this section summarizes the most popular (open) datasets that are used in NLP research. Here, the information about these datasets (names of datasets) was extracted from the analyzed texts, using Named Entity Recognition and Keyphrase search. Let us briefly summarize these two methods.

3.1.1 Named Entity Recognition-NER

Named Entity Recognition (NER) can be seen as finding an answer to “the problem of locating and categorizing important nouns, and proper nouns, in a text” [ 31 ]. Here, automatic methods should facilitate extraction of, among others, named topics, issues, problems, and other “things” mentioned in texts (e.g. in articles). Hence, the spaCy [ 32 ] NER model “en-core-web-lg” ⑪ has been used to extract named entities. These entities have been linked by co-occurrence, and visualized as networks (further described in section 3.4).

SpaCy has been chosen over other models (e.g. transformers [ 33 ] pipeline ⑫ ), because it was simpler to use, and performed faster.

3.1.2 Key phrase Search

Another simple and effective way of extracting information from text, is keyword and/or keyphrase search [ 34 , 35 ]. This technique can be used not only in the preliminary exploratory data analysis (EDA), but also to extract actual and useful findings. Furthermore, keyphrase search is also complementary to, and extends, results of Named Entity Recognition (NER) (Section 3.1.1).

To apply keyphrase search, first, texts were cleaned with Stage 2 cleaning (see Section 2.2). Second, they were converted to phrases (n-grams) of lengths 1-4. Next, two exhaustive lists were created, based on all phrases (n-grams): (a) allowed phrases (609 terms), and (b) banned phrases (1235 terms). The allowed phrases contained word and phrases, which were meaningful for natural language processing or were specific enough to be considered separate, e.g. TF-IDF, accuracy, annotation, NER, taxonomy. The list of banned phrases contains words and phrases, which on their own carried no significant meaning for this research, e.g. bad, big, bit, long, power, index, default. The banned phrases also contained some incoherent phrases, which slipped through the previous cleaning phases. These lists were used to filter the phrases found in the texts. Obtained results were converted to networks of phrase co-occurrence, to visualize phrase importance, and relations between phrases.

3.1.3 Approaches to finding names of most popular NLP datasets

Keyword search was used to extract names of NLP datasets used in collected papers. To properly factor out dataset names and omit noise words, two approaches were applied: unsupervised and list-based.

Unsupervised approach included extracting words (proper nouns detected with Python spaCy ⑬ library) in the near neighborhood (max 3 words before or after) of words “data”, “dataset” and similar.

In list-based approaches, the algorithm looked for particular dataset names that were identified in the three big aggregated lists of NLP datasets ⑭ ⑮ ⑯ .

3.1.4 Findings Related to RQI; What are the Most Popular NLP Datasets

This section presents the findings, which answer RQ1, i.e. which datasets are most often used in NLP research. To best show datasets that are popular, and outline which are used together, a heatmap has been created. It is presented in Figure 1 . In general, a heatmap allows getting not only a general ranking of features (looking only at the diagonal), but also provides the information of correlation of features, or lack thereof.

Heatmap of top 10 percentile of NLP datasets co-usage (logarithmic scale).

It can be easily seen that the most popular dataset, used in NLP, is Wikipedia. Among the top 4 most popular datasets, one can find also: Twitter, Facebook, and WordNet. There is a high correlation between use of datasets, which were extracted from Twitter and Facebook, which are very frequently used together. This is both intuitive and observable in articles dedicated to social network analysis [ 36 ], social text sentiment analysis [ 37 ], social media mining [ 38 ] and other social science related texts [ 39 ]. Manual checking determined also that Twitter is extremely popular in sentiment analysis and other emotion-related explorations [ 40 ].

3.2 Findings Related to RQ2: What Languages are Studied in NLP Research

The second research question concerned languages that were analyzed in reported research (not the language the paper was written in). This information was mined using the same two methods, i.e. keyphrase search and NER. The results were represented in two ways. The basic method was a co-occurrence heatmap presented in Figure 2 .

Heatmap of language co-occurrence in articles.

For clarity, the following is the ranking of top 20 most popular languages, by number of papers in which they have been considered:

English: 2215

Chinese: 809

German: 682

French: 533

Spanish: 416

Arabic: 306

Japanese: 299

Italian: 257

Russian: 239

Portuguese: 154

Turkish: 144

Korean: 130

Finnish: 125

Swedish: 125

As it is visible in Figure 2 , the most popular language is English, but it may be caused by the bias of analyzing only English-language-written papers. Next, there is no particular positive, or negative, correlation between languages. However, there are slight negative correlations between languages Basque and Bengali, Irish and Thai, and Thai and Urdu, which means that these languages are very rarely researched together. There are two observations regarding these languages. (1) All of them are niche and do not have a big speaking population. (2) All pairs have very distant geographical origins, so there may be a low demand for their co-studying.

3.3 Findings Related to RQ3: What are the Popular Fields, and Topics, of Research

Let us now discuss the finding related to the most popular fields and topics of reported research. In order to ascertain them, in addition to keyphrase search and NER, metadata mining and text summarization have been applied. Let us now introduce these methods in some detail.

3.3.1 Metadata Mining

In addition to the information available within the text of a publication, further information can be found in its metadata. For instance, the date of publishing, overall categorization, hierarchical topic assignment and more, as discussed in the next paragraphs.

Therefore, metadata has been fetched both from the original source (arXiv API) and from the Semantic Scholar ⑰ . As a result, for each retrieved paper, the following information became available for further analysis:

data: title, abstract and PDF,

metadata: authors, arXiv category and publishing date,

citations/references,

Note that the Semantic Scholar topics are different from the arXiv categories. The arXiv categories follow a set taxonomy ⑱ , which is used by the person who uploads the text. On the other hand, the Semantic Scholar “uses machine language techniques to analyze publications and extract topic keywords that balance diversity, relevance, and coverage relative to our corpus.” ⑲

The metadata from both sources was complete for all articles (there were no missing fields for any of the papers). Obviously, one cannot guarantee that the information itself was correct. This had to be (and was) assumed, to use this data in further analysis.

3.3.2 Matching Literature to Research Topics

In literature review, one may analyze all available information. However, it is much faster to initially check if a particular paper's topic is related to ones planned/ongoing research. Both Semantic Scholar and arXiv provide this information in the metadata. Semantic Scholar provides “topics”, while arXiv provides “categories”.

Figure 3 shows (1) what topics are the most popular (see the first column from the left), and (2) the correlation of topics. The measure used in the heatmap (correlation matrix) is the count of articles tagged with topics (logarithmic scale has been used).

Correlation matrix between top 0.5 percentile of topics (logarithmic scale).

Obviously, the most popular field of research is “Natural Language Processing”. It is also worth mentioning that Artificial intelligence, Machine Learning and Deep Learning also score high in the article count. This is intuitive, as current applications of NLP are pursued using approaches from, broadly understood, artificial intelligence.

Moreover, the correlation, and high score, between “Deep Learning” and “Artificial Neural Networks” mirrors the influence of BERT and similar models. On the other hand, there are topics, which very rarely coincide. These are, for instance, Parsing and Computer Vision, Convolutional Neural Networks and Machine Translation, Speech Recognition and Sentiment analysis.

There is also one topic worth pointing out to: Baseline (configuration management) . According to the Semantic Scholar, it is defined as “an agreed description of the attributes of a product, at a point in time, which serves as a basis for defining change” ⑳ . This topic does not suit the NLP particularly, as it is too vague, and it could have been incorrectly assigned by the machine learning algorithm on the backend of Semantic Scholar.

Yet another interesting aspect is the evolution of topics in time, which gives a wider perspective of what topics are on the rise in, or fall from, popularity. Figures 4 show the most popular categories in time. The category cs.CL (“Computation and Language”) is a dominating in all periods because it is the main subcategory of NLP. However, multiple interesting observation can be made. First, categories that are particularly popular nowadays are: cs.LG (Machine Learning), cs.AI (Artificial Intelligence), cs.CV (Computer Vision and Pattern Recognition). Second, there are categories, which experience a drop in interest. These are: stat. ML (Machine Learning) and cs.NE (Neural and Evolutionary Computing).

Most popular categories in time (top 96 percentile for each time period).

Moving to “categories” from arXiv, it is important to elaborate the difference between them and “topics”. As mentioned, arXiv follows a taxonomy with two levels: primary category (always a single one) and secondary categories (may be many).

To best show this relation, as well as categories’ popularity, a treemap chart has been created, which is most suitable for “nested” category visualization. It is shown in Figure 5 .

Similarly to the Semantic Scholar “topics”, the largest primary category is cs.CL (Computation and Language), which is a counterpart to the NLP topic from the arXiv nomenclature. Its top secondary categories are cs.LG/stat.ML (both categories of Machine Learning) and cs.AI (Artificial Intelligence). This is, again, consistent with previous findings and shows how these domains overlap each other. It is also worth noting the presence of cs.CV (Computer Vision and Pattern Recognition), which, although to a lesser degree, is also important in the NLP literature. Manual verification shows that, in this context, computer vision refers mostly to image description with text [ 41 ], visual question answering [ 42 ], using transformer neural networks for image recognition [ 43 , 44 ], and other image pattern recognition, vaguely related to NLP.

Similarly, as for topics, a trend analysis has been performed for categories. It is presented in Figure 6 . The most popular topic over time is NLP , followed by Artificial neural network, Experiment, Deep learning , and Machine learning. Here, no particular evolution is noticeable, except for rise in interests in the Language model topic.

Simplified treemap visualizing arXiv primary categories aggregating secondary categories. Outer rectangles are primary categories, inner rectangles are other assigned categories. Other categories include primary category to additionally show the primary categories size. Top 20.0 of primary categories and categories. Colors are purely aesthetic.

Most popular topics in time (top 99,8 percentile for each time period).

3.3.3 Citations

Another interesting metainformation, is the citation count [ 45 , 46 ]. Hence, this statistic was used to determine key works, which were then used to establish key research topic in NLP (addressing also RQ1-3).

It is well known that, in most cases, the distribution of node degree in a citation network is exponential [ 47 ]. Specifically, there are many works with 0-1 citations, and very few with more than 10 citations. In this context, the citations network of top 10% of most highly cited papers is depicted in Figure 7 . The most cited papers are 1810.04805v2 [ 48 ] (5760 citations), 1603.04467v2 [ 49 ] (2653 citations) and 1606.05250v3 [ 50 ] (1789 citations). The first one is the introduction of the BERT model. Here, it is easy to notice that this papers absolutely dominates the network in terms of the degree. It is the networks focal point. This means that the whole domain not only revolves around one particular topic, but also around a single paper.

Citation network of all articles (arrows point towards cited paper); top 5 percentile; A→B, means A cites B (B is a reference of A); Color scale indicates how many papers cite a given paper (yellow—higher, dark blue— lower).

The second paper concerns TensorFlow, the state-of-the-art library for neural networks construction and management. The third introduces “Squad”—a text dataset with over 100,000 questions, used for machine learning. It is important to note that these are the top 3 papers when considering not only works published after 2015, but also when the “all time most cited works” are searched for.

How can two papers cite each other. An interesting observation has been made, during the citation analysis. Typically, relation, where one paper quotes another paper, should be one-way. In other words when paper A cites paper B, that means that paper B is a reference for paper A. So the set of citations and reference should be disjoint. This is true for over 95% of works. However, 363 of papers have an intersection between citations and references, with the biggest having even 10 common positions. Further, manual, analysis determined that this “anomaly” happens due to the existence of preprints, and all other cases where a paper appeared publicly (e.g. being a Technical Report) and then was revised and cited a different paper. This may happen, for instance, when a paper is criticised and it is reprinted (an updated version is created) to address the critique.

3.4 RQ3 Related Findings Based on Application of Keyphrase and Entity Networks

As discussed, NER has been used to determine NLP datasets and languages analyzed in papers. It can also be used when looking for techniques used in research. However, to better visualize the topic of interest, it can be combined with network analysis. Specifically, work reported in the literature involves many-to-many relations, which provide information of what techniques, methods, problems, languages etc., are used alone, in tandem or, perhaps, in groups. To properly explore the area, four dimensional networks (see Figures 8 and 9 ) have been constructed, with: nodes (entities), node size (scaled by an attribute), edges (relations), edge width (scaled by an attribute). Moreover, since all networks are exponential and have very high edge density, only the top percentile of entities has been graphically represented (to allow readability). Networks have been built using networkx [ 51 ] and igraph [ 52 ] Python libraries.

Entity network; entities detected using spaCy (en_core_web_lg 3.1.0); edges width—number of papers with the entity; node size and color—sum of weight of edges; top 0.4 percentile of node weight; top 20.0 percentile of edge weight.

Figure 9 shows very popular name entities, but skips the most often found ones. This has been done to allow other frequent terms to become visible. Specifically, the networks were trimmed by node weight, i.e. number of papers including the named entity. The Figure 9 contains terms between the 99.5 and 99.9 percentiles by node weight. In addition to some previously made observations, new entities appeared, which show what is also of considerable interest in NLP literature. These are:

As shown in the Figure 8 the majority of entities are related to models such as BERT, and neural network architectures (e.g. RNN, CNN). However, the findings show not only NLP-related topics, but all entities. Here, an important warning, regarding used NER models, should be stated. In most cases, when NER is applied directly, and without additional techniques, the entities are not disambiguated, or properly unified. For instance, surnames, like, Kim, Wang, Liang, Liu, Chen, etc. are not properly recognized as names of different persons and “bagged together”. Therefore, further interpretation of results of NER may require manual checking of results.

Moreover, corroborating earlier noted result, is that Wikipedia and Twitter, being the most popular data sources for NLP, can be observed.

Finally, among important entities, Association for Computational Linguistics (also shown as “the Association for Computational Linguistics” and “ACL” ㉑ ) has been found. This society organizes conferences, events and also runs a journal about natural language processing.

GPU (Graphic Processing Unit), which are often used to accelerate neural network training (and use) [ 53 ]

WordNet—semantic network “connecting words” with regard to their meaning [ 54 ] ㉒ and ImageNet —a image database using WordNet hierarchy to propose a network of images [ 55 ] ㉓

SemEval—popular contents in NLP, occurring annually and challenging scientist with different NLP tasks ㉔

and other particular methods like (citation contain example papers): Bayesian methods [ 56 ], CBOW (Continuous Bag of Words) [ 57 ], Markov processes [ 58 ]

Entity network; entities detected using spaCy (en_core_web_lg 3.1.0); edges width—number of papers with the entity; node size and color—sum of weight of edges; node weight between 99.5 and 99.9 percentile; top 20.0 percentile of edge weight.

As described in Section 3.1.2, the keyphrase search was used to extract these terms and findings, which might have been skipped in the NER results. For example, the word “accuracy” is a widely used metric in NLP and many other domains. However, it is not a named entity, because it is also an “ordinary” word in English and is not detected as such by the NER models. Applied analysis produced a network of keyphrase co-occurrence. Hence, network visualization was, again, applied ( Figure 10 ). This allowed formulation of hypotheses, which underwent further (positive) manual verification, specifically:

Keyphrase co-occurrence network Node size—article count where keyword appears Node color— citation sum where keyword appears Edge width & color—number of articles in which two terms appeared.

BERT models are most commonly used in their pretrained “version” / “state”. BERT is already a pretrained model, but it is possible to continue its training (to get a better representation of particular language, topic or domain). The second approach is using BERT, or its pretrained variant, to train it on a target task, called downstream task (these techniques is also called “fine-tuning”).

Transformers are connected strongly with attention. This is because transformer (a neural network architecture) is characterized by the presence of attention mechanism in it. This is the distinguishing factor of this architecture [ 59 ].

“Music” is connected with “lyrics”. This shows that the intersection between NLP research and music domain is via lyrics analysis. The lack of correlation between music and other terms shows that audio analysis, sentiment analysis, etc. are not that popular in this context.

“Precision” is connected with “recall” These two extremely popular evaluation metrics for classification are often used together. Their main point is to handle imbalanced datasets, where the performance is not evaluated correctly by the “accuracy” [ 60 ] measure.

“Synset” is connected with “WordNet”. As shown, WordNet is most commonly used with Synset (a user programmer-friendly interface available in the NLTK framework ㉕ ).

Quantum mechanics begins to emerge in NLP. The oldest works in the field of quantum computing (in the set under study) date back to 2013 [ 61 ], but most (>90%) of the recent works dates to 2019-2021. These provide answers to the to problems such as: applying NLP algorithm on “nearly quantum” computers [ 62 ], sentence meaning inference with quantum circuit model(s), encoding-decoding [ 63 ], quantum machine learning [ 64 ] or, even, ready-to-use Python libraries for the quantum NLP [ 65 ] are investigated. There are still very few works joining the worlds of NLP and quantum computing, but their number is significantly growing since 2019.

Graphs are very common in research related to semantic analysis. One of the the domains that NLP overlaps/includes is semantics. The entities network illustrates how important the concept of a graph is in semantics research (e.g. knowledge graphs). Some works touch these topics in tandem with text embedding [ 66 ], text summarization [ 67 ], knowledge extraction/inference/infusion [ 67 ] or question answering [ 68 ].

3.4.1 Text Summarization

Another approach to extract key information (including the field of research) is to reduce the original text to a brief and simple “conclusion”. This can be done with extractive and abstractive summarization methods. Both aim at allowing the user to comprehend the main message of the text. Moreover, depending on what sentences are chosen in the extractive summarization methods, one may find which abstracts (and papers) are most “summaritive”.

Extractive summarization. First, the extractive methods have been used to summarize the text of all abstracts. Specifically, the following methods have been applied.

Luhn methods [ 69 ] (max 5 sentences) shown in Listing 1

Latent Semantic Analysis [ 70 ] (max 5 sentence) shown in Listing 2

LexRank [ 71 ] (max 5 sentence) shown in Listing 3

TextRank [ 72 ] (max 5 sentence) shown in Listing 4

Here, note that, due to formatting errors in the original texts, the library pysummarization ㉖ had trouble with “sentences with periods” (e.g. “3.5% by the two models, respectively.” is only a part of a full sentence, but it contains a period character).

Abstractive summarization. Previous research found that abstractive summarization methods can “understand the sense” of the text, and build its summary [ 73 ]. It was also found that their overall performance is better than that of extractive methods [ 74 ]. However, most state-of-the-art solutions have limitations related to the maximum number of tokens, i.e. BERT-like models (e.g. distilbart-cnn-12-6 model [ 75 ], bart-large-cnn [ 75 ], bert-extractive-summarizer [ 76 ]) support maximum of 512 tokens, while the largest Pegasus model supports 1024 [ 77 ].

Nevertheless, very recent work proposes a transformer model for long text summarization, a “Longformer” [ 78 ], which is designed to summarize texts of 4000 tokens and more. However, this capability comes with a high RAM memory requirement. So, in order to test abstractive methods, Longformer was applied only to titles of most influential texts (top 5% of citation count).

The final note about text summarization is that, most recent research proposed innovative ways to overcome the length issue (see, [ 79 ]). There is thus a possibility to apply text summarization, for instance, to abstracts combined with introduction and conclusions of research papers. Testing this possibility may be a good starting point for research, but is out of scope of this contribution.

3.4.2 Summarization Findings

Listings 1, 2, 3, 4, show summaries of all abstracts and Listing 5 shows summary of all titles (as described in Section 3.4.1).

The common part for all summaries addresses (in a hierarchical order, starting from most popular features):

natural language processing and artificial intelligence,

translation and image processing,

neural networks,

deep neural network architectures, e.g. CNN, RNN, encoder-decoder, transformers, and

deep neural network models, e.g. BERT, ELMO.

Moreover, the main “ideas”, which appear in the summaries are: effectiveness, “state-of-the-art” solutions, and solutions “better than others”. This shows the “competitive” and “progress-focused” nature of the domain. Authors find it necessary to highlight how “good” or “better than” their solution is. It may also mean that there is not much space for “exploratory” and “non-results-oriented” research (at least this is the message permeates the top cited articles). Similarly, research indicating which approaches do not work in a given domain is not appreciated.

Summary with LSA (512.9 sec)

Natural language processing, as a data analytics related technology, is used widely in many research areas such as artificial intelligence, human language processing, and translation. [paper id: 1608.04434v1]

At present, due to explosive growth of data, there are many challenges for natural language processing. [paper id: 1608.04434v1]

Hadoop is one of the platforms that can process the large amount of data required for natural language processing. [paper id: 1608.04434v1]

KOSHIK is one of the natural language processing architectures, and utilizes Hadoop and contains language processing components such as Stanford CoreNLP and OpenNLP. [paper id: 1608.04434v1]

This study describes how to build a KOSHIK platform with the relevant tools, and provides the steps to analyze wiki data. [paper id: 1608.04434v1]

Summary with sumy-LSA (512.9 sec)

Summary with LexRank (11323.26 sec)

Many natural language processing applications use language models to generate text. [paper id: 1511.06732v7]

However, there is no known natural language processing (NLP) work on this language. [paper id: 1912.03444v1]

However, few have been presented in the natural language process domain. [paper id: 2107.07114v1]

Here, we show their effectiveness in natural language processing. [paper id: 2109.04712v1]

The other two methods however, are not as useful. [paper id: 2109.01411v1]

Summary with sumy-TextRank (497.67 sec)

Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference. [paper id: 1901.04085v5]

In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters. [paper id: 2002.12312v1]

We explore how well the model performs on several languages across several tasks: a diagnostic classification probing the embeddings for a particular syntactic property, a cloze task testing the language modelling ability to fill in gaps in a sentence, and a natural language generation task testing for the ability to produce coherent text fitting a given context. [paper id: 1910.03806v1]

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. [paper id: 2010.04249v1]

Transfer learning in natural language processing (NLP), as realized using models like BERT (Bi-directional Encoder Representation from Transformer), has significantly improved language representation with models that can tackle challenging language problems. [paper id: 2104.08335v1]

‘The Natural Language Processing (NLT) is a new tool that can teach people about the world. The tool is based on the data collected by CNN and RNN. A survey of the Usages of Deep Learning was carried out by the 2015 MSCOCO Image Search. It was created by a survey of people in the UK and the US. An image is worth 16x16 words, and a survey reveals how many people are interested in the language.’

3.5 RQ1, RQ2, RQ3: Relations between NLP Datasets, Languages, and Topics of Research

Additionally, to separate results for RQ1, RQ2 and RQ3, there are situations when important information is the coincidence of these three aspects: NLP datasets, languages, and research topics. The triplet dataset-language-problem is usually fixed on two positions. For example, a research may be focused on machine translation (problem) into English (language), but with missing a corpus (dataset); or a group of Chinese researchers (language) has access to a rich Twitter API (dataset), but is considering what type of analysis (problem) is most prominent. This sparks a question what datasets are used, with which languages, and for what problems. Presented results of correlations between these 3 aspects are divided into two groups, for 2 most popular language: English and Chinese. They are shown in Figure 11 . The remaining results for the selected languages, from the most popular ones, can be found in Figure 12 and 13.

Datasets and NLP problems for languages English and Chinese.

Datasets and NLP problems for chosen languages.

For English and Chinese languages (being the subject of NLP research) the distribution of problems is very similar. The top problems are: machine translation, question answering, sentiment analysis and summarization. The most popular dataset used for all of these problems is Wikipedia. Additionally, for sentiment analysis, there is a significant number of contributions that use also Twitter. All of these observations are consistent with previous results (reported in Sections 3.1 3.6 3.2).

Before going into languages other than English and Chinese, it is crucial to recall that this analysis focused only on articles written in English. Hence, reported results may be biased in the case of research devoted to other language(s). Nevertheless, there exists a large body of work about NLP applied to non-English languages, which is written in English. For instance, among all analyzed papers for this contribution, 41% were devoted to NLP in the context of neither English (non-english papers are 46% of the dataset) nor Chinese (non-chinese papers are 80% of the dataset).

The most important observation is that the distribution of problems for languages other than English and Chinese is, overall, similar (Machine Translation, Question-Answering, sentiment and summarization are the most popular ones). However, there are also some distinguishable differences:

For German and French, summarization, language modelling and natural language inference, and named entity recognition are the key research areas.

In Arabic and Italian, Japanese, Polish, Estonian, Swedish and Finish, there is a visible trend of interest in named entity recognition.

Dependency parsing is more pronounced in research on languages such as German, French, Czech, Japanese, Spanish, Slovene, Swahili and Russian.

In Basque, Ukrainian, Bulgarian the domain does not have particular homogeneous subdomain distribution. The problems of interests are: co-reference resolution, dependency parsing, dialogue-focused research, language modeling, machine translation, multitask learning, named entity recognition, natural language inference, part-of-speech tagging, question answering.

In Bengali, a special area of interest is part-of-speech tagging.

Research focused on Catalan have a particular interests in dialogue-related texts.

Research regarding Indonesian have a very high percent of sentiment analysis research. Even higher than most popular topic of machine translation.

Studies on Norwegian language are strongly focused on sentiment analysis, which peeks over the most common domain of most of the languages—machine translation.

Research focusing on Russian puts a special effort in analyzing dialogues and dependency parsing.

There are only minimal difference between datasets used for English and Chinese, and other languages. The key ones are:

Facebook is present as one of the main sources in many languages, being particularly popular data source for: Bengali, and Spanish

Twitter is a key data source in research on languages: Arabic, Dutch, French, German, Hindi, Italian, Korean, Spanish, Tamil

WordNet is very often used in research involving: Moldovan and Romanian

Tibetan language research nearly never uses Twitter as the dataset.

3.6 Findings Concerning RQ4: Most Popular Specific Tasks and Problems

At the heart of the research is yet another key aspect—the specific problem that is being tackled, or the task, which is being solved. This may seem similar to the domain, or to the general direction of the research. However, some general problems contain specific problems (e.g. machine translation and English-Chinese machine translation, or named entity recognition and named entity linking). On the other hand, some specific problems have more complicated relation, e.g. machine translation, which in NLP can be solved using neural networks, but neural networks are also an independent domain on their own, which is also a superdomain (or a subdomain) of, for instance, image recognition. These complicated relations point to the need for a standardized NLP taxonomy. This, however, is also out of scope of this contribution.

Let us come back to the methods of analyzing specific results. To extract most popular specific tasks and particular problems, methods described above, such as NER, keyphrase search, metadata mining, text summarization, and network visualization were used. Before presenting specific results, an important aspect of keyphrase search needs to be mentioned. An unsupervised search for particular specific topics of research cannot be reasonably performed. All approaches of unsupervised keyphrase search that have been tried (in an exploratory fashion) produced thousands of potential results. Therefore, supervised keyphrase search has been applied. Therefore, the NLP problems were determined based on an exhaustive (multilingual) list, aggregating most popular NLP tasks ㉗ .

The list has been extracted from the website and pruned of any additional markdown ㉘ , to obtain a clean text format. Next, all keywords and keyphrases from the text of each paper has been compared with the NLP tasks list. Finally, each paper has been assigned a list of problems found in its text. Figure 14 shows the popularity (by count) of problems addressed in NLP literature.

Again, there is a dominating problem—machine translation. This is very intuitive, if one takes into account the recent studies [ 80 , 81 , 82 , 83 , 84 ] showing that lack of high fidelity machine translation remains the key barrier for world-wide communication. This problem seems very persistent, because it was indicated also in older research (e.g. in text from 1968 [ 85 ]). Here, it is important to recall that this contribution is likely to be biased towards translation involving English language, because it only analyzed English-written literature.

The remaining top 3 most popular problems are question answering [ 86 ] and sentiment analysis [ 87 ]. In both these domains, there are already state-of-the-art models ready to be used ㉙ . What is interesting, for both question answering and sentiment analysis, most of the models are based either on BERT or its variation, DistilBERT [ 88 ].

Histogram of problems tackled in NLP literature.

3.7 RQ5: Seeking Outliers in the NLP Domain

Some scientific research areas are homogeneous, and all publication revolve around similar topic (group of topics). On the other hand, some can be very diverse, with individual papers touching very different subfields. Finally, there are also domains where, from a more or less homogeneous set, a separate, distinguishable, subset can be pointed to. To verify the structure of the field of NLP, two methods have been used. One is, previously introduced, metadata mining. The second one was text embedding and cauterization. Let us briefly introduce the second one.

3.7.1 Text Embeddings

One of ubiquitous methods in text processing are word, sentence and document embeddings. Text embeddings, which “convert texts to numbers”, have been used to determine key differences/similarities between analyzed texts.

Embeddings can be divided into: contextualized and context-less [ 89 ]. Scientific papers often use words, which strongly depend on the context The prime example is the word “BERT” [ 48 ], which on the one hand is a character from a TV show, but in the NLP world it is a name of one of the state-of-the-art embedding models. In this context, envision application of BERT, the NLP method, to analysis of dialogues in children TV, where one of the dialogues would include BERT, the character. Similar situation concerns words like network (either neural network, graph network, social network, or computer network), “spark” [ 90 ] (either a small fiery particle, or the name of a popular Big Data library), lemma (either a proven proposition in logic, or a morphological form of a word), etc. Hence, in this study, using contextualized text embeddings is more appropriate. This being the case, very popular static text embeddings like Glove [ 91 ] and Word2Vec [ 92 , 93 ] have not been used.

There are many libraries and models available for contextualized text embedding, e.g.: transformers [ 33 ], flair [ 94 ], gensim [ 95 ] and models: BERT [ 48 ] (and its variations like Roberta [ 96 ], DistilBERT [ 88 ]), GPT-2 [ 97 ], T5 [ 98 ], ELMo [ 99 ] and others. However, most of them require specific and high-end hardware to operate reasonably fast (i.e. GPU acceleration [ 100 ]). Here, the decision was to proceed with FastText [ 101 ]. FastText is designed to produce time efficient results, which can be recreated on standard hardware. Moreover, it is designed for “text representations and text classifiers” ㉚ , which is exactly what is needed in this work.

3.7.2 Embedding and Clustering

It is important to highlight that since FastText, like most embeddings, has been trained on a pretty noisy data [ 101 ], the input text of articles was preprocessed only with Stage 1 cleaning (see Section 2.2). Next, a grid search [ 102 ] was performed, to tune hyperparameters. While, as noted earlier, hyperparameter tuning has not been applied, use of grid search, reported here, illustrates that there exist ready-to-use libraries that can be applied when hyperparameter tuning is required. Overall, the best embeddings were produced by a model with the following hyperparameters ㉛ :

dimension: 20

minimum subword size: 3

maximum subword size: 6

number of epochs: 5

learning rate: 0.00005

Finally, the FastText model was further trained in an unsupervised mode (which is standard in majority of cases for general language modelling), on texts of papers, to better fit the representation.

After embeddings have been calculated, their vector representations have been clustered. Since there was no response variable, an unsupervised classifier was applied. Again (as in Section 3.7.1), the main goal was simplicity and time efficiency.

Out of all tested algorithms (K-means [ 103 ], OPTICS [ 104 , 105 ], DBSCAN [ 106 , 107 ], HDBSCAN [ 108 ] and Birch [ 109 ]), the best time efficiency, combined with relative simplicity of use, was achieved with K-means (see, also [ 110 , 111 ]). Moreover, in found research, K-means clustering showed best results, when applied to FastText embeddings (see, [ 112 ]).

The evaluation of clustering has been performed using three clustering metrics: Silhouette score [ 113 ], Davies-Bouldin score [ 114 ], Caliński-Harabasz Score [ 115 ]. These metrics were chosen because they allow evaluation of unsupervised clustering. To visualize the results on a 2D plane, the multidimensional FastText vectors were converted with t-distributed stochastic neighbor embedding (T-SNE) method [ 116 , 117 ]. T-SNE has been suggested by text embedding visualizations reported in earlier work [ 118 , 119 ].

3.7.3 RQ5: Outliers Found in the NLP Research

Visualizations of embeddings are shown in Figure 15 .

Note that Figure 15 is mainly aesthetic, as actual relations are rarely visible, when dimension reduction is applied. The number of clusters has been evaluated according to 3 clustering metrics (Silhouette score [ 113 ], Davies-Bouldin score [ 114 ], Cali-ski-Harabasz Score [ 115 ]) and the best clustering score has been achieved for 2 clusters. Hence, further analysis considers separation of the embeddings into 2 clusters. To further explore why these particular embeddings appear in the same group, various tests were performed. First, wordclouds of texts (titles and paper texts) in the clusters have been built. The texts for wordclouds were processed with Stage 2 cleaning. Title wordclouds are shown in Figure 2 , while text wordclouds are shown in Figure 3 .

“The blade of NLP”. A visualization of all paper text embeddings grouped in clusters (dimensionality reduced with T-SNE).

Further, citation count comparison (Figures 16 and 17) and authors were checked for text in both clusters.

Based on the content of Figures 2 , 3 , 16 , 17 , 18 , 19 , 20 , 21 and the author per cluster distribution analysis the following conclusions have been drawn:

Histogram of citation counts in cluster 1 (bigger cluster) - logarithmic scale.

Histogram of citation counts in cluster 0 (smaller cluster) - logarithmic scale.

Last, the differences in topics from Semantic Scholar (Figures 18 and 19) and categories from arXiv (Figures 20 and 21) have been checked.

Histogram of topics counts in cluster 1 (bigger cluster).

Histogram of topics counts in cluster 0 (smaller cluster).

Histogram of categories counts in cluster 1 (bigger cluster).

Histogram of categories counts in cluster 0 (smaller cluster).

There is one specific outlier, this is the cluster of work related to texts embeddings.

Content of texts shows strong topical shift towards deep neural networks.

Categories and topics of clusters are not particularly far away from each other, because their distribution is similar. There is a higher representation of computer vision and information retrieval area in the smaller cluster (cluster 0).

There are no distinguishable authors who are responsible for texts in both clusters.

The distribution of citation counts is similar in both clusters.

Furthermore, manual verification showed that deep neural networks is actually the biggest subdomain of NLP, and it touches upon issues, which do not appear in other works. These issues are strictly related to neural networks (e.g. attention mechanism, network architectures, transfer learning, etc.) They are universal, and their applications play an important role in NLP, but also in other domains (image processing[ 120 ], signal processing [ 121 ], anomaly detection [ 122 ], clinical medicine [ 123 ] and many others [ 124 ]).

3.7.4 “Most Original Papers”

In addition to unsupervised clustering, an additional approach to outlier detection has been applied. Specifically, metadata representing citations/reference information was further analyzed. On the one hand, of the “citation spectrum” are the most influential works (as shown in Section 3.3.3). On the other side, there are papers that either are new and have not been cited yet, or those that do not have high influence.

However, the true “original” works are papers which have many citations (they are in top 2 percentile), but very few references (bottom 2 percentile). Based on performed analysis, it was found that such papers are:

“Natural Language Processing (almost) from Scratch” [ 125 ]—a neural network approach to learning internal representations of text, based on unlabeled training data. A similar idea was used in future publications, especially, the most cited paper about BERT model [ 48 ].

“Experimental Support for a Categorical Compositional Distributional Model of Meaning” [ 126 ]—a paper about “modelling compositional meaning for sentences using empirical distributional methods”.

“Gaussian error linear units (gelus)” [ 127 ]—paper introducing GELU, a new activation function in neural networks, which was extensively tested in future research [ 128 ].

Each of these papers introduced novel, very innovative ideas that inspired further research directions. They can be thus treated as belonging to a unique (separate) subset of contributions.

3.8 RQ6: Text Comprehension

Finally, an additional aspect of text belonging to the dataset was measured; text comprehensibility. This is a very complicated problem, which is still being explored. Taking into account that one of the considered audiences are researchers interested in starting work in NLP, text difficulty, using existing text complexity metrics, was evaluated. An important note is that these metrics are known for problems, such as: not considering complicated mathematical formula; skipping charts, pictures and other visuals. Keeping this in mind, let us proceed further.

3.8.1 Text Complexity

The most common comprehensibility measures map text to school grade, in the American education system [ 129 ]. In this way, it is established what is the expected level of reader that should be able to understand the text. The used measures were:

Flesch Reading Ease [ 130 ]

Flesch Kincaid Grade [ 130 ]

Gunning Fog [ 131 ]

Smog Index [ 132 ]

Automated Readability Index [ 130 ]

Coleman Liau Index [ 133 ]

Linsear Write Formula [ 134 ]

All measures return results on equal scale (school grade). Furthermore, they were all consistent in terms of paper scores. To provide the least biased results, the numerical values (Section 3.8.2) have been averaged to achieve a single, straightforward, measure for text complexity. Here, it should be noted that this was done also because delving into discussion of ultimate validity of individual comprehensibility measurements and pros/cons of each of them is out of scope of current contribution. Rather, the combined measure was calculated to obtain a general idea as to the “readability” of the literature in question.

The results can be averaged together between metrics, because all of they refer to the same scale (school grade).

3.8.2 RQ6: Establishing Complexity Level of NLP Literature

Results of the text complexity (RQ6) are rather intuitive.

As shown in Figure 22 , the averaged score of 15 comprehensibility metrics suggests that the majority of papers, in the NLP domain, can be understood by a person after “15th grade”. This matches roughly a person who finished the “1st stage” of college education (engineering studies, bachelor degree, and similar). Obviously, this result shows that use of such metrics to “scientific texts” has limited applicability, as they are based mostly on syntactic features of the text, while the semantics makes some of them difficult to follow even for the specialists. This, particularly, applies to texts which contain mathematical equations, which are being removed during text preprocessing.

Average reading grade (mean of all metrics; bottom 99th percentile) histogram showing what grade should the reader be to understand the papers.

3.9 Summary of Key Results

Let us now summarize the key finding, in the form of a question-answer for each of RQs that have been postulated in Section 1.

The datasets used most commonly for NLP research are: Wikipedia, Twitter, Facebook, WordNet, arXiv, Academic, SST (The Stanford Sentiment Treebank), SQuAD (The Stanford Question Answering Dataset), NLI and SNLI (Stanford Natural Language Inference Corpus), COCO (Common Objects in Context), Reddit.

RQ2: Which languages, other than English, appear as a topic of NLP research?

Languages analyzed most commonly in NLP research, apart from English and Chinese, are: German, French and Spanish.

The most popular fields studied in NLP literature are: Natural Language Processing/Language Computing, artificial intelligence, machine learning, neural networks and deep learning and text embedding.

Particular tasks and problems, which appear in the literature, are: text embedding with BERT and transformers, machine translation between English and other languages (especially English-Chinese), sentiment analysis (most popular with Twitter and Wikipedia datasets), question answering models (with Wikipedia and SQuAD datasets), named entity recognition, and text summarization.

According to the text embedding analysis, there is not enough evidence to find a strongly distinguishable clusters. Hence, there are no outstanding subgroups in the NLP literature.

According to averaged standard comprehensibility measures, scientific texts related to NLP can be digested by a 15th graders, which maps to the 3rd year of higher education (e.g. College, Bachelor's degree studies etc.)

This analysis used Natural Language Processing methods to analyze scientific literature related to NLP. The goal was to answer 6 research questions (RQ1-RQ6). A total of 4712 scientific papers in the field of NLP from arXiv were analyzed. The work used and illustrated at the same time the following NLP methods: text extraction, text cleaning, text preprocessing, keyword and keyphrase search, text embeddings, abstractive and extractive text summarization, text complexity and other methods such as: clustering, metadata analysis, citation/reference analysis, network visualization. This analysis focuses on only Natural Language Processing and its subdomains, topics, etc. Since the procedures of obtaining results reported here were fully automated, the same or similar analysis could be analogically done with ease for different literature languages and even fields. Hence, all the tools used for the analysis are available in a designated repository ㉜ for future applications.

https://jupyter.org

https://pypi.org

http://labs.jstor.org/api/docs

Specifically, the query had the form http://export.arxiv.org/api/query?search_query=all:%22natural%20language%20processing%22start=0&amp;max_results=10000 . Since such query may take long time to load; to reduce time, one can change the value of the max_results parameter to a smaller number, e.g. 5

https://pdfminersix.readthedocs.io/en

https://github.com/euske/pdfminer

https://github.com/HazyResearch/pdftotree

https://www.crummy.com/software/BeautifulSoup

https://dumps.wikimedia.org

https://commoncrawl.org

https://github.com/explosion/spacy-models/releases/tag/en_core_webJg-3.2.0

https://huggingface.co/transformers/main_classes/pipelines.html#tokenclassificationpipeline

https://spacy.io

https://metatext.io/datasets

https://github.com/niderhoff/nlp-datasets

https://github.com/karthikncode/nlp-datasets

https://www.semanticscholar.org

https://arxiv.org/category_taxonomy

https://www.semanticscholar.org/faq#extract-key-phrases

https://www.semanticscholar.org/topic/Baseline-(configuration-management)/3403

https://www.aclweb.org

https://wordnet.princeton.edu

https://image-net.org

https://semeval.github.io

https://www.nltk.org/howto/wordnet.html

https://pypi.org/project/pysummarization

https://github.com/sebastianruder/NLP-progress

https://www.markdownguide.org

https://huggingface.co/models?language=enpipeline_tag=question-answering

https://fasttext.cc

https://fasttext.cc/docs/en/options.html

https://anonymous.4open.science/r/nlp-review-F81D

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9 Natural Language Processing Trends in 2023

Are you curious about which natural language processing trends & startups will soon impact your business? Explore our in-depth industry research on 1 645 NLP startups & scaleups and get data-driven insights into technology-based solutions in our Natural Language Processing Innovation Map!

Natural language processing (NLP) is a subset of AI which finds growing importance due to the increasing amount of unstructured language data. The rapid growth of social media and digital data creates significant challenges in analyzing vast user data to generate insights. Further, interactive automation systems such as chatbots are unable to fully replace humans due to their lack of understanding of semantics and context. To tackle these issues, natural language models are utilizing advanced machine learning (ML) to better understand unstructured voice and text data. This article provides an overview of the top global natural language processing trends in 2023. They range from virtual agents and sentiment analysis to semantic search and reinforcement learning.

Innovation Map outlines the Top 9 Natural Language Processing Trends & 18 Promising Startups

For this in-depth research on the Top Natural Language Processing Trends & Startups, we analyzed a sample of 1 645 global startups & scaleups. The result of this research is data-driven innovation intelligence that improves strategic decision-making by giving you an overview of emerging technologies & startups advancing data processing. These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform , covering 2 500 000+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant startups, emerging technologies & future industry trends quickly & exhaustively.

In the Innovation Map below, you get an overview of the Top 9 Natural Language Processing Trends & Innovations that impact 1 645 companies worldwide. Moreover, the Natural Language Processing Innovation Map reveals 18 hand-picked startups, all working on emerging technologies that advance their field.

Top 9 Natural Language Processing Trends

• Virtual Assistants
• Sentiment Analysis
• Multilingual Language Models
• Named Entity Recognition
• Language Transformers
• Transfer Learning
• Text Summarization
• Semantic Search
• Reinforcement Learning

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Tree Map reveals the Impact of the Top 9 Natural Language Processing Trends

Based on the Natural Language Processing Innovation Map, the Tree Map below illustrates the impact of the Top 9 NLP Trends in 2023. Virtual assistants improve customer relationships and worker productivity through smarter assistance functions. Advances in learning models, such as reinforced and transfer learning, are reducing the time to train natural language processors. Besides, sentiment analysis and semantic search enable language processors to better understand text and speech context. Named entity recognition (NER) works to identify names and persons within unstructured data while text summarization reduces text volume to provide important key points. Language transformers are also advancing language processors through self-attention. Lastly, multilingual language models use machine learning to analyze text in multiple languages.

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Global Startup Heat Map covers 1 645 Natural Language Processing Startups & Scaleups

The Global Startup Heat Map below highlights the global distribution of the 1 645 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform, the Heat Map reveals that the US sees the most startup activity.

Below, you get to meet 18 out of these 1 645 promising startups & scaleups as well as the solutions they develop. These natural language processing startups are hand-picked based on criteria such as founding year, location, funding raised, & more. Depending on your specific needs, your top picks might look entirely different.

Top 8 Natural Language Processing Trends in 2023

1. virtual assistants.

There is a growing interest in virtual assistants in devices and applications as they improve accessibility and provide information on demand. However, they deliver accurate information only if the virtual assistants understand the query without misinterpretation. That is why startups are leveraging NLP to develop novel virtual assistants and chatbots. They mitigate processing errors and work continuously, unlike human virtual assistants. Additionally, NLP-powered virtual assistants find applications in providing information to factory workers, assisting academic research, and more.

Servicely advances Intelligent Service Management

Australian startup Servicely develops Sofi , an AI-powered self-service automation software solution. Its self-learning AI engine uses plain English to observe and add to its knowledge, which improves its efficiency over time. This allows Sofi to provide employees and customers with more accurate information. The flexible low-code, virtual assistant suggests the next best actions for service desk agents and greatly reduces call-handling costs.

Vox automates Conversational Experiences

Vox is a Malaysian startup that automates conversational experiences. The startup’s virtual assistant engages with customers over multiple channels and devices as well as handles various languages. Besides, its conversational AI uses predictive behavior analytics to track user intent and identifies specific personas. This enables businesses to better understand their customers and personalize product or service offerings.

2. Sentiment Analysis

Our increasingly digital world generates exponential amounts of data as audio, video, and text. While natural language processors are able to analyze large sources of data, they are unable to differentiate between positive, negative, or neutral speech. Moreover, when support agents interact with customers, they are able to adapt their conversation based on the customers’ emotional state which typical NLP models neglect. Therefore, startups are creating NLP models that understand the emotional or sentimental aspect of text data along with its context. Such NLP models improve customer loyalty and retention by delivering better services and customer experiences.

Y Meadows provides AI-based Customer Support

US-based startup Y Meadows automates customer support requests using AI. The startup’s customer service automation solution collects data from customers through multiple channels, such as emails and web forms, and understands human intent. Its deep learning-based NLP model perceives message context instead of focusing on keywords. Y Meadow’s semantics-based solution finds use across industries for customer issue handling.

Spiky delivers Video Sentiment Analytics

Spiky is a US startup that develops an AI-based analytics tool to improve sales calls, training, and coaching sessions. The startup’s automated coaching platform for revenue teams uses video recordings of meetings to generate engagement metrics. It also generates context and behavior-driven analytics and provides various unique communication and content-related metrics from vocal and non-verbal sources. This way, the platform improves sales performance and customer engagement skills of sales teams.

3. Multilingual Language Models

Communication is highly complex, with over 7000 languages spoken across the world, each with its own intricacies. Most current natural language processors focus on the English language and therefore either do not cater to the other markets or are inefficient. The availability of large training datasets in different languages enables the development of NLP models that accurately understand unstructured data in different languages. This improves data accessibility and allows businesses to speed up their translation workflows and increase their brand reach.

Lingoes offers No-Code Multilingual Text Analytics

Finnish startup Lingoes makes a single-click solution to train and deploy multilingual NLP models. It features intelligent text analytics in 109 languages and features automation of all technical steps to set up NLP models. Additionally, the solution integrates with a wide range of apps and processes as well as provides an application programming interface (API) for special integrations. This enables marketing teams to monitor customer sentiments, product teams to analyze customer feedback, and developers to create production-ready multilingual NLP classifiers.

NLP Cloud provides Pre-trained Multilingual AI Models

NLP Cloud is a French startup that creates advanced multilingual AI models for text understanding and generation. They feature custom models, customization with GPT-J, follow HIPPA, GDPR, and CCPA compliance, and support many languages. Besides, these language models are able to perform summarization, entity extraction, paraphrasing, and classification. NLP Cloud’s models thus overcome the complexities of deploying AI models into production while mitigating in-house DevOps and machine learning teams.

4. Named Entity Recognition

Data classification and annotation are important for a wide range of applications such as autonomous vehicles, recommendation systems, and more. However, classifying data from unstructured data proves difficult for nearly all traditional processing algorithms. Named entity recognition (NER) is a language processor that removes these limitations by scanning unstructured data to locate and classify various parameters. Besides identifying person names, organizations, brands, etc. NER classifies dates and times, email addresses, and numerical measurements like money and weight. NER models thus facilitate data extraction workflows across industries.

M47AI enables AI-based Data Annotation

Spanish startup M47AI offers an AI-based data annotation platform to improve data labeling. It uses NER to identify and categorize names, locations, etc. The platform also tags words based on grammar, part of speech, function, and definition. It then performs entity linking to connect entity mentions in the text with a predefined set of relational categories. Besides improving data labeling workflows, the platform reduces time and cost through intelligent automation.

HyperGlue simplifies Unstructured Text Data Analysis

HyperGlue is a US-based startup that develops an analytics solution to generate insights from unstructured text data. It utilizes natural language processing techniques such as topic clustering, NER, and sentiment reporting. Companies use the startup’s solution to discover anomalies and monitor key trends from customer data.

5. Language Transformers

Natural language solutions require massive language datasets to train processors. This training process deals with issues, like similar-sounding words, that affect the performance of NLP models. Language transformers avoid these by applying self-attention mechanisms to better understand the relationships between sequential elements. Moreover, this type of neural network architecture ensures that the weighted average calculation for each word is unique.

Build & Code aids Construction Document Processing

German startup Build & Code uses NLP to process documents in the construction industry. The startup’s solution uses language transformers and a proprietary knowledge graph to automatically compile, understand, and process data. It features automatic documentation matching, search, and filtering as well as smart recommendations. This solution consolidates data from numerous construction documents, such as 3D plans and bills of materials (BOM), and simplifies information delivery to stakeholders.

Birch.AI advances Call Center Operation Automation

Birch.AI is a US-based startup that specializes in AI-based automation of call center operations. The startup’s solution utilizes transformer-based NLPs with models specifically built to understand complex, high-compliance conversations. This includes healthcare, insurance, and banking applications. Birch.AI’s proprietary end-to-end pipeline uses speech-to-text during conversations. It also generates a summary and applies semantic analysis to gain insights from customers. The startup’s solution finds applications in challenging customer service areas such as insurance claims, debt recovery, and more.

6. Transfer Learning

Machine learning tasks are domain-specific and models are unable to generalize their learning. This causes problems as real-world data is mostly unstructured, unlike training datasets. Consequently, this affects the predictability of the trained models. However, many language models are able to share much of their training data using transfer learning to optimize the general process of deep learning. The application of transfer learning in natural language processing significantly reduces the time and cost to train new NLP models.

Got It AI creates Autonomous Conversational AI

US-based startup Got It AI offers a conversational AI platform that improves customer experience management. It uses transfer learning and NLP models with transformers such as BERT, GPT-3, and T5. Moreover, its product suite, AutoFlow , identifies the conversational paths that virtual agents follow and uses historical conversation data to improve customer engagement.

QuillBot enables AI-powered Paraphrasing

QuillBot is a US-based startup that makes an AI-powered paraphrasing tool. It uses natural language generation (NLG) and transfer learning to power its customizable text slider and AI-powered thesaurus that suggests synonyms. The tool also checks grammar, creates summaries, generates citations, and checks plagiarism. Additionally, it integrates directly into Google Chrome and Microsoft Word to enable better, faster, and smarter writing.

7. Text Summarization

Natural language processors are extremely efficient at analyzing large datasets to understand human language as it is spoken and written. However, typical NLP models lack the ability to differentiate between useful and useless information when analyzing large text documents. Therefore, startups are applying machine learning algorithms to develop NLP models that summarize lengthy texts into a cohesive and fluent summary that contains all key points. The main befits of such language processors are the time savings in deconstructing a document and the increase in productivity from quick data summarization.

SummarizeBot offers Blockchain-powered Summaries

Latvian startup SummarizeBot develops a blockchain-based platform to extract, structure, and analyze text. It leverages AI to summarize information in real time, which users share via Slack or Facebook Messenger. Besides, it provides summaries of audio content within a few seconds and supports multiple languages. SummarizeBot’s platform thus finds applications in academics, content creation, and scientific research, among others.

Zeon AI Labs provides an Intelligent Search Platform

Zeon AI Labs is an Indian startup that makes a summary generator. The startup’s summarization solution, DeepDelve , uses NLP to provide accurate and contextual answers to questions based on information from enterprise documents. Additionally, it supports search filters, multi-format documents, autocompletion, and voice search to assist employees in finding information. The startup’s other product, IntelliFAQ , finds answers quickly for frequently asked questions and features continuous learning to improve its results. These products save time for lawyers seeking information from large text databases and provide students with easy access to information from educational libraries and courseware.

8. Semantic Search

Search engines are an integral part of workflows to find and receive digital information. One of the barriers to effective searches is the lack of understanding of the context and intent of the input data. NLP enables semantic search queries that analyze search intent. This improves search accuracy and provides more relevant results. Hence, semantic search models find applications in areas such as eCommerce, academic research, enterprise knowledge management, and more.

deepset builds Natural Language Interfaces

German startup deepset develops a cloud-based software-as-a-service (SaaS) platform for NLP applications. It features all the core components necessary to build, compose, and deploy custom natural language interfaces, pipelines, and services. The startup’s NLP framework, Haystack , combines transformer-based language models and a pipeline-oriented structure to create scalable semantic search systems. Moreover, the quick iteration, evaluation, and model comparison features reduce the cost for companies to build natural language products.

Vectara develops an ML-based Search Pipeline

Vectara is a US-based startup that offers a neural search-as-a-service platform to extract and index information. It contains a cloud-native, API-driven, ML-based semantic search pipeline, Vectara Neural Rank , that uses large language models to gain a deeper understanding of questions. Moreover, Vectara’s semantic search requires no retraining, tuning, stop words, synonyms, knowledge graphs, or ontology management, unlike other platforms.

9. Reinforcement Learning

Currently, NLP-based solutions struggle when dealing with situations outside of their boundaries. Therefore, AI models need to be retrained for each specific situation that it is unable to solve, which is highly time-consuming. Reinforcement learning enables NLP models to learn behavior that maximizes the possibility of a positive outcome through feedback from the environment. This enables developers and businesses to continuously improve their NLP models’ performance through sequences of reward-based training iterations. Such learning models thus improve NLP-based applications such as healthcare and translation software, chatbots, and more.

AyGLOO creates Explainable AI

Spanish startup AyGLOO creates an explainable AI solution that transforms complex AI models into easy-to-understand natural language rule sets. The startup applies AI techniques based on proprietary algorithms and reinforcement learning to receive feedback from the front web and optimize NLP techniques. AyGLOO’s solution finds applications in customer lifetime value (CLV) optimization, digital marketing, and customer segmentation, among others.

VeracityAI specializes in Natural Language Model Training

VeracityAI is a Ghana-based startup specializing in product design, development, and prototyping using AI, ML, and deep learning. The startup’s reinforcement learning-based recommender system utilizes an experience-based approach that adapts to individual needs and future interactions with its users. This not only optimizes the efficiency of solving cold start recommender problems but also improves recommendation quality.

Discover all Natural Language Processing Trends, Technologies & Startups

Machine learning models such as reinforcement learning, transfer learning, and language transformers drive the increasing implementation of NLP systems. Text summarization, semantic search, and multilingual language models expand the use cases of NLP into academics, content creation, and so on. The cost and resource-efficient development of NLP solutions is also a necessary requirement to increase their adoption.

The Natural Language Processing Trends & Startups outlined in this report only scratch the surface of trends that we identified during our data-driven innovation & startup scouting process. Among others, transfer learning, semantic web, and behavior analysis will transform the sector as we know it today. Identifying new opportunities & emerging technologies to implement into your business goes a long way in gaining a competitive advantage. Get in touch to easily & exhaustively scout startups, technologies & trends that matter to you!

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211 Research Topics in Linguistics To Get Top Grades

Many people find it hard to decide on their linguistics research topics because of the assumed complexities involved. They struggle to choose easy research paper topics for English language too because they think it could be too simple for a university or college level certificate.

All that you need to learn about Linguistics and English is sprawled across syntax, phonetics, morphology, phonology, semantics, grammar, vocabulary, and a few others. To easily create a top-notch essay or conduct a research study, you can consider this list of research topics in English language below for your university or college use. Note that you can fine-tune these to suit your interests.

Linguistics Research Paper Topics

If you want to study how language is applied and its importance in the world, you can consider these Linguistics topics for your research paper. They are:

• An analysis of romantic ideas and their expression amongst French people
• An overview of the hate language in the course against religion
• Identify the determinants of hate language and the means of propagation
• Evaluate a literature and examine how Linguistics is applied to the understanding of minor languages
• Consider the impact of social media in the development of slangs
• An overview of political slang and its use amongst New York teenagers
• Examine the relevance of Linguistics in a digitalized world
• Analyze foul language and how it’s used to oppress minors
• Identify the role of language in the national identity of a socially dynamic society
• Attempt an explanation to how the language barrier could affect the social life of an individual in a new society
• Discuss the means through which language can enrich cultural identities
• Examine the concept of bilingualism and how it applies in the real world
• Analyze the possible strategies for teaching a foreign language
• Discuss the priority of teachers in the teaching of grammar to non-native speakers
• Choose a school of your choice and observe the slang used by its students: analyze how it affects their social lives
• Attempt a critical overview of racist languages
• What does endangered language means and how does it apply in the real world?
• A critical overview of your second language and why it is a second language
• What are the motivators of speech and why are they relevant?
• Analyze the difference between the different types of communications and their significance to specially-abled persons
• Give a critical overview of five literature on sign language
• Evaluate the distinction between the means of language comprehension between an adult and a teenager
• Consider a native American group and evaluate how cultural diversity has influenced their language
• Analyze the complexities involved in code-switching and code-mixing
• Give a critical overview of the importance of language to a teenager
• Attempt a forensic overview of language accessibility and what it means
• What do you believe are the means of communications and what are their uniqueness?
• Attempt a study of Islamic poetry and its role in language development
• Attempt a study on the role of Literature in language development
• Evaluate the Influence of metaphors and other literary devices in the depth of each sentence
• Identify the role of literary devices in the development of proverbs in any African country
• Cognitive Linguistics: analyze two pieces of Literature that offers a critical view of perception
• Identify and analyze the complexities in unspoken words
• Expression is another kind of language: discuss
• Identify the significance of symbols in the evolution of language
• Discuss how learning more than a single language promote cross-cultural developments
• Analyze how the loss of a mother tongue affect the language Efficiency of a community
• Critically examine how sign language works
• Using literature from the medieval era, attempt a study of the evolution of language
• Identify how wars have led to the reduction in the popularity of a language of your choice across any country of the world
• Critically examine five Literature on why accent changes based on environment
• What are the forces that compel the comprehension of language in a child
• Identify and explain the difference between the listening and speaking skills and their significance in the understanding of language
• Give a critical overview of how natural language is processed
• Examine the influence of language on culture and vice versa
• It is possible to understand a language even without living in that society: discuss
• Identify the arguments regarding speech defects
• Discuss how the familiarity of language informs the creation of slangs
• Explain the significance of religious phrases and sacred languages
• Explore the roots and evolution of incantations in Africa

Sociolinguistic Research Topics

You may as well need interesting Linguistics topics based on sociolinguistic purposes for your research. Sociolinguistics is the study and recording of natural speech. It’s primarily the casual status of most informal conversations. You can consider the following Sociolinguistic research topics for your research:

• What makes language exceptional to a particular person?
• How does language form a unique means of expression to writers?
• Examine the kind of speech used in health and emergencies
• Analyze the language theory explored by family members during dinner
• Evaluate the possible variation of language based on class
• Evaluate the language of racism, social tension, and sexism
• Discuss how Language promotes social and cultural familiarities
• Give an overview of identity and language
• Examine why some language speakers enjoy listening to foreigners who speak their native language
• Give a forensic analysis of his the language of entertainment is different to the language in professional settings
• Give an understanding of how Language changes
• Examine the Sociolinguistics of the Caribbeans
• Consider an overview of metaphor in France
• Explain why the direct translation of written words is incomprehensible in Linguistics
• Discuss the use of language in marginalizing a community
• Analyze the history of Arabic and the culture that enhanced it
• Discuss the growth of French and the influences of other languages
• Examine how the English language developed and its interdependence on other languages
• Give an overview of cultural diversity and Linguistics in teaching
• Challenge the attachment of speech defect with disability of language listening and speaking abilities
• Explore the uniqueness of language between siblings
• Explore the means of making requests between a teenager and his parents
• Observe and comment on how students relate with their teachers through language
• Observe and comment on the communication of strategy of parents and teachers
• Examine the connection of understanding first language with academic excellence

Language Research Topics

Numerous languages exist in different societies. This is why you may seek to understand the motivations behind language through these Linguistics project ideas. You can consider the following interesting Linguistics topics and their application to language:

• What does language shift mean?
• Discuss the stages of English language development?
• Examine the position of ambiguity in a romantic Language of your choice
• Why are some languages called romantic languages?
• Observe the strategies of persuasion through Language
• Discuss the connection between symbols and words
• Identify the language of political speeches
• Discuss the effectiveness of language in an indigenous cultural revolution
• Trace the motivators for spoken language
• What does language acquisition mean to you?
• Examine three pieces of literature on language translation and its role in multilingual accessibility
• Identify the science involved in language reception
• Interrogate with the context of language disorders
• Examine how psychotherapy applies to victims of language disorders
• Study the growth of Hindi despite colonialism
• Critically appraise the term, language erasure
• Examine how colonialism and war is responsible for the loss of language
• Give an overview of the difference between sounds and letters and how they apply to the German language
• Explain why the placement of verb and preposition is different in German and English languages
• Choose two languages of your choice and examine their historical relationship
• Discuss the strategies employed by people while learning new languages
• Discuss the role of all the figures of speech in the advancement of language
• Analyze the complexities of autism and its victims
• Offer a linguist approach to language uniqueness between a Down Syndrome child and an autist
• Express dance as a language
• Express music as a language
• Express language as a form of language
• Evaluate the role of cultural diversity in the decline of languages in South Africa
• Discuss the development of the Greek language
• Critically review two literary texts, one from the medieval era and another published a decade ago, and examine the language shifts

Linguistics Essay Topics

You may also need Linguistics research topics for your Linguistics essays. As a linguist in the making, these can help you consider controversies in Linguistics as a discipline and address them through your study. You can consider:

• The connection of sociolinguistics in comprehending interests in multilingualism
• Write on your belief of how language encourages sexism
• What do you understand about the differences between British and American English?
• Discuss how slangs grew and how they started
• Consider how age leads to loss of language
• Review how language is used in formal and informal conversation
• Discuss what you understand by polite language
• Discuss what you know by hate language
• Evaluate how language has remained flexible throughout history
• Mimicking a teacher is a form of exercising hate Language: discuss
• Body Language and verbal speech are different things: discuss
• Language can be exploitative: discuss
• Do you think language is responsible for inciting aggression against the state?
• Can you justify the structural representation of any symbol of your choice?
• Religious symbols are not ordinary Language: what are your perspective on day-to-day languages and sacred ones?
• Consider the usage of language by an English man and someone of another culture
• Discuss the essence of code-mixing and code-switching
• Attempt a psychological assessment on the role of language in academic development
• How does language pose a challenge to studying?
• Choose a multicultural society of your choice and explain the problem they face
• What forms does Language use in expression?
• Identify the reasons behind unspoken words and actions
• Why do universal languages exist as a means of easy communication?
• Examine the role of the English language in the world
• Examine the role of Arabic in the world
• Examine the role of romantic languages in the world
• Evaluate the significance of each teaching Resources in a language classroom
• Consider an assessment of language analysis
• Why do people comprehend beyond what is written or expressed?
• What is the impact of hate speech on a woman?
• Do you believe that grammatical errors are how everyone’s comprehension of language is determined?
• Observe the Influence of technology in language learning and development
• Which parts of the body are responsible for understanding new languages
• How has language informed development?
• Would you say language has improved human relations or worsened it considering it as a tool for violence?
• Would you say language in a black populous state is different from its social culture in white populous states?
• Give an overview of the English language in Nigeria
• Give an overview of the English language in Uganda
• Give an overview of the English language in India
• Give an overview of Russian in Europe
• Give a conceptual analysis on stress and how it works
• Consider the means of vocabulary development and its role in cultural relationships
• Examine the effects of Linguistics in language
• Present your understanding of sign language
• What do you understand about descriptive language and prescriptive Language?

List of Research Topics in English Language

You may need English research topics for your next research. These are topics that are socially crafted for you as a student of language in any institution. You can consider the following for in-depth analysis:

• Examine the travail of women in any feminist text of your choice
• Examine the movement of feminist literature in the Industrial period
• Give an overview of five Gothic literature and what you understand from them
• Examine rock music and how it emerged as a genre
• Evaluate the cultural association with Nina Simone’s music
• What is the relevance of Shakespeare in English literature?
• How has literature promoted the English language?
• Identify the effect of spelling errors in the academic performance of students in an institution of your choice
• Critically survey a university and give rationalize the literary texts offered as Significant
• Examine the use of feminist literature in advancing the course against patriarchy
• Give an overview of the themes in William Shakespeare’s “Julius Caesar”
• Express the significance of Ernest Hemingway’s diction in contemporary literature
• Examine the predominant devices in the works of William Shakespeare
• Explain the predominant devices in the works of Christopher Marlowe
• Charles Dickens and his works: express the dominating themes in his Literature
• Why is Literature described as the mirror of society?
• Examine the issues of feminism in Sefi Atta’s “Everything Good Will Come” and Bernadine Evaristos’s “Girl, Woman, Other”
• Give an overview of the stylistics employed in the writing of “Girl, Woman, Other” by Bernadine Evaristo
• Describe the language of advertisement in social media and newspapers
• Describe what poetic Language means
• Examine the use of code-switching and code-mixing on Mexican Americans
• Examine the use of code-switching and code-mixing in Indian Americans
• Discuss the influence of George Orwell’s “Animal Farm” on satirical literature
• Examine the Linguistics features of “Native Son” by Richard Wright
• What is the role of indigenous literature in promoting cultural identities
• How has literature informed cultural consciousness?
• Analyze five literature on semantics and their Influence on the study
• Assess the role of grammar in day to day communications
• Observe the role of multidisciplinary approaches in understanding the English language
• What does stylistics mean while analyzing medieval literary texts?
• Analyze the views of philosophers on language, society, and culture

English Research Paper Topics for College Students

For your college work, you may need to undergo a study of any phenomenon in the world. Note that they could be Linguistics essay topics or mainly a research study of an idea of your choice. Thus, you can choose your research ideas from any of the following:

• The concept of fairness in a democratic Government
• The capacity of a leader isn’t in his or her academic degrees
• The concept of discrimination in education
• The theory of discrimination in Islamic states
• The idea of school policing
• A study on grade inflation and its consequences
• A study of taxation and Its importance to the economy from a citizen’s perspectives
• A study on how eloquence lead to discrimination amongst high school students
• A study of the influence of the music industry in teens
• An Evaluation of pornography and its impacts on College students
• A descriptive study of how the FBI works according to Hollywood
• A critical consideration of the cons and pros of vaccination
• The health effect of sleep disorders
• An overview of three literary texts across three genres of Literature and how they connect to you
• A critical overview of “King Oedipus”: the role of the supernatural in day to day life
• Examine the novel “12 Years a Slave” as a reflection of servitude and brutality exerted by white slave owners
• Rationalize the emergence of racist Literature with concrete examples
• A study of the limits of literature in accessing rural readers
• Analyze the perspectives of modern authors on the Influence of medieval Literature on their craft
• What do you understand by the mortality of a literary text?
• A study of controversial Literature and its role in shaping the discussion
• A critical overview of three literary texts that dealt with domestic abuse and their role in changing the narratives about domestic violence
• Choose three contemporary poets and analyze the themes of their works
• Do you believe that contemporary American literature is the repetition of unnecessary themes already treated in the past?
• A study of the evolution of Literature and its styles
• The use of sexual innuendos in literature
• The use of sexist languages in literature and its effect on the public
• The disaster associated with media reports of fake news
• Conduct a study on how language is used as a tool for manipulation
• Attempt a criticism of a controversial Literary text and why it shouldn’t be studied or sold in the first place

Finding Linguistics Hard To Write About?

With these topics, you can commence your research with ease. However, if you need professional writing help for any part of the research, you can scout here online for the best research paper writing service.

There are several expert writers on ENL hosted on our website that you can consider for a fast response on your research study at a cheap price.

As students, you may be unable to cover every part of your research on your own. This inability is the reason you should consider expert writers for custom research topics in Linguistics approved by your professor for high grades.

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Research: How Ratings Systems Shape User Behavior in the Gig Economy

• Arne De Keyser,
• Christophe Lembregts,
• Jeroen Schepers

A study reveals surprising differences between displaying an average score or individual reviews.

Platform providers typically display ratings information to the user in two ways. Incremental rating systems, employed by platforms like TaskRabbit and Airbnb, offer a detailed view by listing and often providing insights into every individual review score. Averaged rating systems, used by platforms such as Uber, Lyft, and DoorDash, present an overall score that aggregates all individual ratings. Over a series of nine experiments, researchers found that the way low ratings are communicated shapes user experience and behavior in a number of ways. Their findings offer implications for companies choosing between incremental or average ratings systems.

Rating systems, integral to the platform economy, profoundly influence human behavior and choice. Platforms like Uber, Airbnb, Turo, and Upwork rely on these systems not just as reflections of past performance, but as proactive tools for ensuring quality and encouraging proper conduct on both sides of a transaction from service providers (such as drivers and hosts) and users (like riders and guests).

• AK Arne De Keyser is a professor of marketing at EDHEC Business School. His research focuses on customer experience, frontline service technologies, and circular services.
• CL Christophe Lembregts is an associate professor of marketing at RSM Erasmus University. His research focuses on facilitating informed decision-making by investigating responses to quantitative information.
• JS Jeroen Schepers is an associate professor of frontline service and innovation at Eindhoven University of Technology. His research centers on frontline employees, artificial intelligence, and service robots.

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Two key brain systems are central to psychosis, Stanford Medicine-led study finds

When the brain has trouble filtering incoming information and predicting what’s likely to happen, psychosis can result, Stanford Medicine-led research shows.

April 11, 2024 - By Erin Digitale

People with psychosis have trouble filtering relevant information (mesh funnel) and predicting rewarding events (broken crystal ball), creating a complex inner world. Emily Moskal

Inside the brains of people with psychosis, two key systems are malfunctioning: a “filter” that directs attention toward important external events and internal thoughts, and a “predictor” composed of pathways that anticipate rewards.

Dysfunction of these systems makes it difficult to know what’s real, manifesting as hallucinations and delusions. 

The findings come from a Stanford Medicine-led study , published April 11 in  Molecular Psychiatry , that used brain scan data from children, teens and young adults with psychosis. The results confirm an existing theory of how breaks with reality occur.

“This work provides a good model for understanding the development and progression of schizophrenia, which is a challenging problem,” said lead author  Kaustubh Supekar , PhD, clinical associate professor of psychiatry and behavioral sciences.

The findings, observed in individuals with a rare genetic disease called 22q11.2 deletion syndrome who experience psychosis as well as in those with psychosis of unknown origin, advance scientists’ understanding of the underlying brain mechanisms and theoretical frameworks related to psychosis.

During psychosis, patients experience hallucinations, such as hearing voices, and hold delusional beliefs, such as thinking that people who are not real exist. Psychosis can occur on its own and isa hallmark of certain serious mental illnesses, including bipolar disorder and schizophrenia. Schizophrenia is also characterized by social withdrawal, disorganized thinking and speech, and a reduction in energy and motivation.

It is challenging to study how schizophrenia begins in the brain. The condition usually emerges in teens or young adults, most of whom soon begin taking antipsychotic medications to ease their symptoms. When researchers analyze brain scans from people with established schizophrenia, they cannot distinguish the effects of the disease from the effects of the medications. They also do not know how schizophrenia changes the brain as the disease progresses. 

To get an early view of the disease process, the Stanford Medicine team studied young people aged 6 to 39 with 22q11.2 deletion syndrome, a genetic condition with a 30% risk for psychosis, schizophrenia or both. 

Kaustubh Supekar

Brain function in 22q11.2 patients who have psychosis is similar to that in people with psychosis of unknown origin, they found. And these brain patterns matched what the researchers had previously theorized was generating psychosis symptoms.

“The brain patterns we identified support our theoretical models of how cognitive control systems malfunction in psychosis,” said senior study author  Vinod Menon , PhD, the Rachael L. and Walter F. Nichols, MD, Professor; a professor of psychiatry and behavioral sciences; and director of the  Stanford Cognitive and Systems Neuroscience Laboratory .

Thoughts that are not linked to reality can capture the brain’s cognitive control networks, he said. “This process derails the normal functioning of cognitive control, allowing intrusive thoughts to dominate, culminating in symptoms we recognize as psychosis.”

Cerebral sorting  

Normally, the brain’s cognitive filtering system — aka the salience network — works behind the scenes to selectively direct our attention to important internal thoughts and external events. With its help, we can dismiss irrational thoughts and unimportant events and focus on what’s real and meaningful to us, such as paying attention to traffic so we avoid a collision.

The ventral striatum, a small brain region, and associated brain pathways driven by dopamine, play an important role in predicting what will be rewarding or important. 

For the study, the researchers assembled as much functional MRI brain-scan data as possible from young people with 22q11.2 deletion syndrome, totaling 101 individuals scanned at three different universities. (The study also included brain scans from several comparison groups without 22q11.2 deletion syndrome: 120 people with early idiopathic psychosis, 101 people with autism, 123 with attention deficit/hyperactivity disorder and 411 healthy controls.) 

The genetic condition, characterized by deletion of part of the 22nd chromosome, affects 1 in every 2,000 to 4,000 people. In addition to the 30% risk of schizophrenia or psychosis, people with the syndrome can also have autism or attention deficit hyperactivity disorder, which is why these conditions were included in the comparison groups.

The researchers used a type of machine learning algorithm called a spatiotemporal deep neural network to characterize patterns of brain function in all patients with 22q11.2 deletion syndrome compared with healthy subjects. With a cohort of patients whose brains were scanned at the University of California, Los Angeles, they developed an algorithmic model that distinguished brain scans from people with 22q11.2 deletion syndrome versus those without it. The model predicted the syndrome with greater than 94% accuracy. They validated the model in additional groups of people with or without the genetic syndrome who had received brain scans at UC Davis and Pontificia Universidad Católica de Chile, showing that in these independent groups, the model sorted brain scans with 84% to 90% accuracy.

The researchers then used the model to investigate which brain features play the biggest role in psychosis. Prior studies of psychosis had not given consistent results, likely because their sample sizes were too small. 

Vinod Menon

Comparing brain scans from 22q11.2 deletion syndrome patients who had and did not have psychosis, the researchers showed that the brain areas contributing most to psychosis are the anterior insula (a key part of the salience network or “filter”) and the ventral striatum (the “reward predictor”); this was true for different cohorts of patients.

In comparing the brain features of people with 22q11.2 deletion syndrome and psychosis against people with psychosis of unknown origin, the model found significant overlap, indicating that these brain features are characteristic of psychosis in general.

A second mathematical model, trained to distinguish all subjects with 22q11.2 deletion syndrome and psychosis from those who have the genetic syndrome but without psychosis, selected brain scans from people with idiopathic psychosis with 77.5% accuracy, again supporting the idea that the brain’s filtering and predicting centers are key to psychosis.

Furthermore, this model was specific to psychosis: It could not classify people with idiopathic autism or ADHD.

“It was quite exciting to trace our steps back to our initial question — ‘What are the dysfunctional brain systems in schizophrenia?’ — and to discover similar patterns in this context,” Menon said. “At the neural level, the characteristics differentiating individuals with psychosis in 22q11.2 deletion syndrome are mirroring the pathways we’ve pinpointed in schizophrenia. This parallel reinforces our understanding of psychosis as a condition with identifiable and consistent brain signatures.” However, these brain signatures were not seen in people with the genetic syndrome but no psychosis, holding clues to future directions for research, he added.

Applications for treatment or prevention

In addition to supporting the scientists’ theory about how psychosis occurs, the findings have implications for understanding the condition — and possibly preventing it.

“One of my goals is to prevent or delay development of schizophrenia,” Supekar said. The fact that the new findings are consistent with the team’s prior research on which brain centers contribute most to schizophrenia in adults suggests there may be a way to prevent it, he said. “In schizophrenia, by the time of diagnosis, a lot of damage has already occurred in the brain, and it can be very difficult to change the course of the disease.”

“What we saw is that, early on, functional interactions among brain regions within the same brain systems are abnormal,” he added. “The abnormalities do not start when you are in your 20s; they are evident even when you are 7 or 8.”

Our discoveries underscore the importance of approaching people with psychosis with compassion.

The researchers plan to use existing treatments, such as transcranial magnetic stimulation or focused ultrasound, targeted at these brain centers in young people at risk of psychosis, such as those with 22q11.2 deletion syndrome or with two parents who have schizophrenia, to see if they prevent or delay the onset of the condition or lessen symptoms once they appear. 

The results also suggest that using functional MRI to monitor brain activity at the key centers could help scientists investigate how existing antipsychotic medications are working. 

Although it’s still puzzling why someone becomes untethered from reality — given how risky it seems for one’s well-being — the “how” is now understandable, Supekar said. “From a mechanistic point of view, it makes sense,” he said.

“Our discoveries underscore the importance of approaching people with psychosis with compassion,” Menon said, adding that his team hopes their work not only advances scientific understanding but also inspires a cultural shift toward empathy and support for those experiencing psychosis. 

“I recently had the privilege of engaging with individuals from our department’s early psychosis treatment group,” he said. “Their message was a clear and powerful: ‘We share more similarities than differences. Like anyone, we experience our own highs and lows.’ Their words were a heartfelt appeal for greater empathy and understanding toward those living with this condition. It was a call to view psychosis through a lens of empathy and solidarity.”

Researchers contributed to the study from UCLA, Clinica Alemana Universidad del Desarrollo, Pontificia Universidad Católica de Chile, the University of Oxford and UC Davis.

The study was funded by the Stanford Maternal and Child Health Research Institute’s Uytengsu-Hamilton 22q11 Neuropsychiatry Research Program, FONDEYCT (the National Fund for Scientific and Technological Development of the government of Chile), ANID-Chile (the Chilean National Agency for Research and Development) and the U.S. National Institutes of Health (grants AG072114, MH121069, MH085953 and MH101779).

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Artificial intelligence

Exploring ways AI is applied to health care

Bridging humanities research and federal legislation

As director of the Kluge Center at the Library of Congress, 
Kevin Butterfield plays a key role in educating lawmakers.

When Kevin Butterfield, PhD ’10, organizes presentations for members of Congress and their staffs, he leaves journalists off the guest list.  

Butterfield oversees the John W. Kluge Center at the Library of Congress, a humanities research center that houses more than 100 scholars selected annually to mine the library’s vast resources. As director, one of Butterfield’s duties is to organize two series of dinners — for both members of Congress and Capitol Hill staffers to meet with the scholars and learn about new research related to domestic and foreign policy. 

“Without media present, members of Congress don’t have to worry about sound bites,” Butterfield says. “We always try to move quickly to Q&A. There’s a lot of genuine intellectual curiosity on display. They’re eager to learn.” 

From WashU to Washington

Who : Kevin Butterfield, PhD ’10

Where : At the Kluge Center, he oversees a fellowship program that also provides research presentations to members of Congress.

Path to D.C. : After earning a ­doctorate, he taught history and constitutional studies at the University of Oklahoma for eight years before heading to the George Washington Presidential Library at Mount Vernon, in Virginia.

Butterfield understands the deep connections between intellectual communities and government in American history. At WashU, he wrote a dissertation about the rise of American voluntary associations like churches, fraternities and labor unions, which would later become the award-winning book The Making of Tocqueville’s America . “I was interested in how people in the post-Revolutionary period came together to pursue shared goals and become more collectively than they could be individually,” Butterfield says. “At the Kluge Center, we’re creating a scholarly community that strengthens the work of our individual scholars while also impacting policy.”

Butterfield came to the Library of Congress in September 2022 from Mount Vernon, where he oversaw George Washington’s presidential library. Now a mentor to junior scholars, Butterfield draws on his transformational experience of working with dissertation adviser David Konig, emeritus professor of law and history. “Anytime I went to David’s office, he always made me feel like I had his undivided attention,” Butterfield recalls. “I try to emulate that at the Kluge Center.”

As an early American historian, Butterfield quickly found that his understanding of Capitol Hill needed an update. “It’s no exaggeration to say that when I started, I knew far more about the First Federal Congress than I did about the 117th Congress that was then in session,” Butterfield says. “There are a lot of staff here at the library who know everything about Congress and can help me navigate professional relationships.”

WashU gave Butterfield the language he needed to cross disciplinary and political boundaries, integrate the center within the library’s complex collections, and create the kind of intellectual community that lets Capitol Hill ask difficult questions about history and society. 

“At WashU, I learned how to converse with literary theorists, sociologists and anthropologists in ways that I wouldn’t have been able to master otherwise,” he says. “As a civil servant, I use that knowledge to bridge the gap between research and legislation.” 

Comments and respectful dialogue are encouraged, but content will be moderated. Please, no personal attacks, obscenity or profanity, selling of commercial products, or endorsements of political candidates or positions. We reserve the right to remove any inappropriate comments. We also cannot address individual medical concerns or provide medical advice in this forum.

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April 16, 2024

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New research could enable more—and more efficient—synthesis of metastable materials

by Paul Dailing, University of Chicago

Ion exchange is a powerful technique for converting one material to another when synthesizing new products. In this process, scientists know what reactants lead to what products, but how the process works—the exact pathway of how one material can be converted to another—has remained elusive.

In a paper published in Nature Materials , a team of UChicago Pritzker School of Molecular Engineering researchers shed new light on this mystery. In researching lithium cathode materials for battery storage , a team from the Liu Lab has shown that there is a general pathway for lithium and sodium ion exchange in layered oxide cathode materials.

"We systematically explored the ion exchange process in lithium and sodium," said first author Yu Han, a Ph.D. candidate at PME. "The ion exchange pathway we revealed is new."

By helping explain how the ion exchange process works, this paper opens the doors for researchers working with metastable materials, meaning materials that aren't currently in their most stable possible forms. It can also lead to new innovations in atom-efficient manufacturing, using less of the starting precursors and generating less waste when synthesizing materials.

"It will broaden the family of metastable materials people can synthesize," said PME Asst. Prof. Chong Liu.

New methods

Although the potential applications resonate throughout material synthesis, the paper started by looking at production of lithium for battery cathodes. As climate change pushes the world away from fossil fuels, more and better batteries are needed to store renewable power.

"The old method of solid-state synthesis would be you pick some salt which contains the elements you are looking to synthesize. Then you combine them with the right ratio of each of the element," Liu said. "Then you burn it."

Burning the lithium precursors at 800–900 degrees Celsius is more effective when working with stable materials, however. In cases when the metastable form had interesting properties that could theoretically make great battery cathodes, the high temperatures pushed the materials into a new state that was more stable, but often lacking the interesting properties.

Ion exchange, however, is a synthesis method that can be done at room temperature or at relatively low temperatures of 100 degrees Celsius.

"Room temperature ion exchange allows us to access those metastable layered oxides, which could not be directly synthesized through solid-state synthesis at elevated temperature but might equipped with unique chemical and physical properties," Han said.

In ion exchange, the salts aren't burned but dissolved, letting ions that have the same charge replace unwanted ions. It allows researchers to vary chemical composition while maintaining a solid framework—only the ions are being swapped out. But this too had its drawbacks. The process has historically been resource-intensive and is based on trial and error.

The insights from the PME team's paper will enable researchers to predict not only the final compositions and phases, but also the intermediate states to map out the kinetic pathways.

The PME researchers have already turned their insights on the ion exchange pathways into practice, creating what Han called "a very efficient way" to synthesize lithium (Li) from sodium (Na) and back again. The paper demonstrates the synthesis of pure phase sodium cobalt oxide from the parent lithium cobalt oxide for the first time and also lithium cobalt oxide from sodium cobalt oxide at 1-1000 Li-Na (molar ratio) with electrochemical assisted ion exchange method by mitigating the kinetic barriers.

The team hopes future innovators will go further, creating more efficient, less wasteful processes for synthesizing materials humanity needs for climate change or other pressing global needs.

"In manufacturing now, people are emphasizing atomic efficiency, which means to use the least amount of material to get to what you want," Liu said.

Journal information: Nature Materials

Provided by University of Chicago

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