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representation

Definition of representation

Examples of representation in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'representation.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

15th century, in the meaning defined at sense 1

Phrases Containing representation

• proportional representation
• self - representation

Dictionary Entries Near representation

representant

representationalism

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“Representation.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/representation. Accessed 2 May. 2024.

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[Latin repraesentare ‘to make present or manifest’]

1. Depicting or ‘making present’ something which is absent (e.g. people, places, events, or abstractions) in a different form: as in paintings, photographs, films, or language, rather than as a replica . See also description; compare absent presence.

2. The function of a sign or symbol of ‘standing for’ that to which it refers (its referent).

3. The various processes of production involved in generating representational texts in any medium, including the mass media (e.g. the filming, editing, and broadcasting of a television documentary). Such framings of the concept privilege authorial intention. See also auteur theory; authorial determinism; sender-oriented communication.

4. A text (in any medium) which is the product of such processes, usually regarded as amenable to textual analysis (‘a representation’).

5. What is explicitly or literally described, depicted, or denoted in a sign, text, or discourse in any medium as distinct from its symbolic meaning, metaphoric meaning, or connotations: its manifest referential content, as in ‘a representation of…’ See also mimesis; naturalism; referentiality.

6. How (in what ways) something is depicted. However ‘realistic’ texts may seem to be, they involve some form of transformation. Representations are unavoidably selective (none can ever ‘show the whole picture’), and within a limited frame, some things are foregrounded and others backgrounded: see also framing; generic representation; selective representation; stylization. In factual genres in the mass media, critics understandably focus on issues such as truth, accuracy, bias, and distortion ( see also reflectionism), or on whose realities are being represented and whose are being denied. See also dominant ideology; manipulative model; stereotyping; symbolic erasure.

7. The relation of a sign or text in any medium to its referent. In reflectionist framings, the transparent re- presentation, reflection, recording, transcription, or reproduction of a pre-existing reality ( see also imaginary signifier; mimesis; realism). In constructionist framings, the transformation of particular social realities, subjectivities, or identities in processes which are ostensibly merely re- presentations ( see also constitutive models; interpellation; reality construction). Some postmodern theorists avoid the term representation completely because the epistemological assumptions of realism seem to be embedded within it.

8. A cycle of processes of textual and meaning production and reception situated in a particular sociohistorical context ( see also circuit of communication; circuit of culture). This includes the active processes in which audiences engage in the interpretation of texts ( see also active audience theory; beholder's share; picture perception). Semiotics highlights representational codes which need to be decoded ( see also encoding/decoding model; photographic codes; pictorial codes; realism), and related to a relevant context ( see also Jakobson's model).

9. (narratology) Showing as distinct from telling (narration).

10. (mental representation) The process and product of encoding perceptual experience in the mind: see dual coding theory; gestalt laws; mental representation; perceptual codes; selective perception; selective retention.

11. A relationship in which one person (a representative) acting on behalf of another (as in law), or a political principle in which one person acts, in some sense, on behalf of a group of people, normally having been chosen by them to do so (as in representative democracies).

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representation noun 1

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What does the noun representation mean?

There are 19 meanings listed in OED's entry for the noun representation , three of which are labelled obsolete. See ‘Meaning & use’ for definitions, usage, and quotation evidence.

representation has developed meanings and uses in subjects including

How common is the noun representation ?

How is the noun representation pronounced, british english, u.s. english, where does the noun representation come from.

Earliest known use

Middle English

The earliest known use of the noun representation is in the Middle English period (1150—1500).

OED's earliest evidence for representation is from around 1450, in St. Elizabeth of Spalbeck .

representation is of multiple origins. Either (i) a borrowing from French. Or (ii) a borrowing from Latin.

Etymons: French representation ; Latin repraesentātiōn- , repraesentātiō .

Nearby entries

• reprehensory, adj. 1576–1825
• repremiation, n. 1611
• represent, n. a1500–1635
• represent, v.¹ c1390–
• re-present, v.² 1564–
• representable, adj. & n. 1630–
• representamen, n. 1677–
• representance, n. 1565–
• representant, n. 1622–
• representant, adj. 1851–82
• representation, n.¹ c1450–
• re-presentation, n.² 1805–
• representational, adj. 1850–
• representationalism, n. 1846–
• representationalist, adj. & n. 1846–
• representationary, adj. 1856–
• representationism, n. 1842–
• representationist, n. & adj. 1842–
• representation theory, n. 1928–
• representative, adj. & n. a1475–
• representative fraction, n. 1860–

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Meaning & use

Pronunciation, compounds & derived words, entry history for representation, n.¹.

representation, n.¹ was revised in December 2009.

representation, n.¹ was last modified in March 2024.

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Revisions and additions of this kind were last incorporated into representation, n.¹ in March 2024.

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Representation in English dictionary

Meanings and definitions of "representation".

• That which represents another.
• A figure, image or idea that substitutes reality.
• A theatrical performance.
• noun That which represents another.
• noun (law) The lawyers and staff who argue on behalf of another in court.
• noun (politics) The ability to elect a representative to speak on one's behalf in government; the role of this representative in government.
• noun (mathematics) An object that describes an abstract group in terms of linear transformations of vector spaces; ( more formally ) a homomorphism from a group on a vector space to the general linear group (group of all bijective linear transformations) on the space.
• noun A figure, image or idea that substitutes reality.
• noun A theatrical performance.
• that which represents another
• a figure, image or idea that substitutes reality
• theatrical performance
• Any conduct or action undertaken on behalf of a person, group, business or government, often as an elected or appointed voice.
• acting (principal, etc.)
• noun an activity that stands as an equivalent of something or results in an equivalent
• noun a body of legislators that serve in behalf of some constituency; "a Congressional vacancy occurred in the representation from California"
• noun a creation that is a visual or tangible rendering of someone or something
• noun a factual statement made by one party in order to induce another party to enter into a contract; "the sales contract contains several representations by the vendor"
• noun a performance of a play
• noun a presentation to the mind in the form of an idea or image
• noun a statement of facts and reasons made in appealing or protesting; "certain representations were made concerning police brutality"
• noun the act of representing; standing in for someone or some group and speaking with authority in their behalf
• noun the right of being represented by delegates who have a voice in some legislative body
• noun the state of serving as an official and authorized delegate or agent

Synonyms of "Representation" in English dictionary

public presentation, mental representation, creation are the top synonyms of "Representation" in English thesaurus.

• public presentation · mental representation · creation · body · theatrical · agency · state · mental object · theatrical performance · delegacy · performance · histrionics · cooperation · cognitive content · right · internal representation · activity · content · statement

a performance of a play

a presentation to the mind in the form of an idea or image

the state of serving as an official and authorized delegate or agent

Grammar and declension of Representation

• lm   liczba mnoga representations
• representation ( plural   representations )
• representation ( countable and uncountable , plural representations )
• Representation

Images with "Representation"

Sample sentences with " representation ", available translations.

• Ancient Greek (to 1453)
• Eastern Mari
• Eastern Yiddish
• Interlingua
• Interlingue
• Komo (Democratic Republic of Congo)
• Kurdish Kurmanji
• Lingua Franca Nova
• Min Nan Chinese
• Modern Gaulish
• Northern Sami
• Norwegian Nynorsk
• Pennsylvania German
• Scottish Gaelic
• Swiss German
• Tosk Albanian
• Western Frisian

Meaning Representation

• First Online: 15 November 2023

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• Raymond S. T. Lee 2  

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Before the study of semantic analysis, this chapter explores meaning representation, a vital component in NLP before the discussion of semantic and pragmatic analysis. It studies four major meaning representation techniques which include: first-order predicate calculus (FOPC), semantic net, conceptual dependency diagram (CDD), and frame-based representation. After that it explores canonical form and introduces Fillmore’s theory of universal cases followed by predicate logic and inference work using FOPC with live examples.

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Lee, R.S.T. (2024). Meaning Representation. In: Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1999-4_5

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1. Introduction

3. family trees from translations, 4. comparing languages, 5. analysis of similarities, 6. causal inference, 7. discussion and conclusions, what do language representations really represent.

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Johannes Bjerva , Robert Östling , Maria Han Veiga , Jörg Tiedemann , Isabelle Augenstein; What Do Language Representations Really Represent?. Computational Linguistics 2019; 45 (2): 381–389. doi: https://doi.org/10.1162/coli_a_00351

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A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.

Words can be represented with distributed word representations, currently often in the form of word embeddings. Similarly to how words can be embedded, so can languages, by associating each language with a real-valued vector known as a language representation , which can be used to measure similarities between languages. This type of representation can be obtained by, for example, training a multilingual model for some NLP task (Johnson et al. 2017 ; Malaviya, Neubig, and Littell 2017 ; Östling and Tiedemann 2017 ). The focus of this work is on the evaluation of similarities between such representations. This is an important area of work, as computational approaches to typology (Dunn et al. 2011 ; Cotterell and Eisner 2017 ; Bjerva and Augenstein 2018 ) have the potential to answer research questions on a much larger scale than traditional typological research (Haspelmath 2001 ). Furthermore, having knowledge about the relationships between languages can help in NLP applications (Ammar et al. 2016 ), and having incorrect interpretations can be detrimental to multilingual NLP efforts. For instance, if the similarities between languages in an embedded language space were to be found to encode geographical distances ( Figure 1 ), any conclusions drawn from use of these representations would not likely be of much use for most NLP tasks. The importance of having deeper knowledge of what such representations encapsulate is further hinted at by both experiments with interpolation of language vectors (Östling and Tiedemann 2017 ), as well as multilingual translation models (Johnson et al. 2017 ).

Language representations in a two-dimensional space. What do their similarities represent?

RQ1 . In what way do different language representations encode language similarities? In particular, is genetic similarity what is really captured?

RQ2 . What causal relations can we find between language representation similarities?

1.1 Contributions

Our work is most closely related to Rabinovich, Ordan, and Wintner ( 2017 ), who investigate representation learning on monolingual English sentences, which are translations from various source languages to English from the Europarl corpus (Koehn 2005 ). They use a feature-engineering approach to predict source languages and learn an Indo-European family tree using their language representations, showing that there are significant traces of the source languages in translations. They use features based on sequences of part-of-speech (POS) tags, function words, and cohesive markers. Additionally, they posit that the similarities found between their representations encode the genetic relationships between languages. We show that this is not the strongest explanation of the similarities, as a novel syntactic measure offers far more explanatory value, which we further substantiate by investigating causal relationships between language representations and similarities (Pearl 2009 ). This is an important finding as it highlights the need for thoroughly substantiating linguistic claims made based on empirical findings. Further, understanding what similarities are encoded in language embeddings provides insights into how language embeddings could be used for downstream multilingual NLP tasks. If language representations are used for transfer learning to low-resource languages, having an incorrect view of the structure of the language representation space can be dangerous. For instance, the standard assumption of genetic similarity would imply that the representation of the Gagauz language (Turkic, spoken mainly in Moldova) should be interpolated from the genetically very close Turkish, but this would likely lead to poor performance in syntactic tasks because the two languages have diverged radically in syntax relatively recently.

Figure 2 illustrates the data and problem we consider in this paper. We are given a set of English gold-standard translations from the official languages of the European Union, based on speeches from the European Parliament. 1 We wish to learn language representations based on these data, and investigate the linguistic relationships that hold between the resulting representations ( RQ1 ). It is important to abstract away from the surface forms of the translations as, for example, speakers from certain regions will tend to talk about the same issues, or places. We therefore introduce three levels of abstraction: (i) training on function words and POS; (ii) training on only POS tags (POS in Figure 2 ); (iii) training on sequences of dependency relation tags (DepRel in Figure 2 ), and constituent tags. This annotation is obtained using UDPipe (Straka, Hajic, and Straková 2016 ).

Problem illustration. Given official translations from EU languages to English, we train multilingual language models on various levels of abstractions, encoding the source languages. The resulting source language representations ( L raw , etc.) are evaluated.

2.1 Language Representations

For each level of abstraction, we train a multilingual neural language model in order to obtain representations (vectors in ℝ k ) that we can analyze further ( RQ1 ). Note that this model is multilingual in the sense that we model the source language of each input sequence, whereas the input sequences themselves are, for example, sequences of POS tags. Our model is a multilingual language model using a standard two-layer long short-term memory architecture. Multilinguality is approached similarly to Östling and Tiedemann ( 2017 ), who include a language representation at each time-step. That is to say, each input is represented both by a symbol representation, c , and a language representation, l ∈ L . Because the set of language representations L is updated during training, the resulting representations encode linguistic properties of the languages. Whereas Östling and Tiedemann ( 2017 ) model hundreds of languages, we model only English—however, we redefine L to be the set of source languages from which our translations originate.

We now consider the language representations obtained from training our neural language model on the input sequences with different representations of the text (characters, POS sequences, etc.). We cluster the language representations—vectors in ℝ k —hierarchically 2 and compute similarities between our generated trees and the gold tree of Serva and Petroni ( 2008 ), using the distance metric from Rabinovich, Ordan, and Wintner ( 2017 ). 3 Our generated trees yield comparable results to previous work ( Table 1 ).

Language Modeling using Lexical Information and POS Tags .

Our first experiments deal with training directly on the raw translated texts. This is likely to bias representations by speakers from different countries talking about specific issues or places (as in Figure 2 ), and gives the model comparatively little information to work with as there is no explicit syntactic information available. As a consequence of the lack of explicit syntactic information, it is unsurprising that the results ( LM-Raw in Table 1 ) only marginally outperform the random baseline.

To abstract away from the content and negate the geographical effect we train a new model on only function words and POS. This performs almost on par with LM-Raw ( LM-Func in Table 1 ), indicating that the level of abstraction reached is not sufficient to capture similarities between languages. We next investigate whether we can successfully abstract away from the content by removing function words, and only using POS tags ( LM-POS in Table 1 ). Although Rabinovich, Ordan, and Wintner ( 2017 ) produce sensible trees by using trigrams of POS and function words, we do not obtain such trees in our most similar settings. One hypothesis for why this is the case is the differing architectures used—indicating that our neural architecture does not pick up on the trigram-level statistics present in their explicit feature representations.

Language Modeling on Phrase Structure Trees and Dependency Relations .

To force the language model to predict as much syntactic information as possible, we train on bracketed phrase structure trees. Note that this is similar to the target side of Vinyals et al. ( 2015 ). All content words are replaced by POS tags, and function words are kept. This results in a vocabulary of 289 items (phrase and POS tags and function words). Syntactic information captures more relevant information for reconstructing trees than previous settings ( LM-Phrase in Table 1 ), yielding trees of similar quality to previous work.

We also compare to the Universal Dependencies (UD) formalism, as we train the language model on tuples encoding the dependency relation and POS tag of a word, the head direction, and the head POS tag ( LM-Deprel in Table 1 ). The LM-Phrase and LM-Deprel models yield the best results overall, due to their having access to higher levels of abstraction via syntax. The fact that sufficient cues for the source languages can be found here shows that source language affects the grammatical constructions used (cf. Gellestam 1986 ).

Our main contribution is to investigate whether genetic distance between languages is captured by language representations, or if other distance measures provide more explanation ( RQ1 ). Having shown that our language representations can reproduce genetic trees on par with previous work, we now compare the language embeddings using three different types of language distance measures: genetic distance estimated by methods from historical linguistics, geographical distance of speaker communities, and a novel measure for the structural distances between languages.

4.1 Genetic Distance

Clustering based on dependency link statistics from UD (left), and the genetic tree from Serva and Petroni ( 2008 ) (right). Which type of similarity do language representations really represent?

4.2 Geographical Distance

We rely on the coordinates provided by Glottolog (Hammarström, Forkel, and Haspelmath 2017 ). These are by necessity approximate, because the geography of a language cannot accurately be reduced to a single point denoting the geographical center point of where its speakers live. Still, this provides a way of testing the influence of geographical factors such as language contact or political factors affecting the education system.

4.3 Structural Distance

To summarize the structural properties of each language, we use counts of dependency links from the UD treebanks, version 2.1 (Nivre et al. 2017 ). Specifically, we represent each link by combining head and dependent POS, dependency type, and direction. This yields 8,607 combinations, so we represent each language by a 8,607-dimensional normalized vector, and compute the cosine distance between these language representations.

Figure 3 shows the result of clustering these vectors (Ward clustering, cosine distance). Although strongly correlated with genealogical distance, significant differences can be observed. Romanian, as a member of the Balkan sprachbund, is distinct from the other Romance languages. The North Germanic (Danish, Swedish) and West Germanic (Dutch, German) branches are separated through considerable structural differences, with English grouped with the North Germanic languages despite its West Germanic origin. The Baltic languages (Latvian, Lithuanian) are grouped with the nearby Finnic languages (Estonian, Finnish) rather than their distant Slavic relatives.

This idea has been explored previously by Chen and Gerdes ( 2017 ), who use a combination of relative frequency, length, and direction of deprels. We, by comparison, achieve an even richer representation by also taking head and dependent POS into account.

Although we are able to reconstruct phylogenetic language trees in a similar manner to previous work, we wish to investigate whether genetic relationships between languages really is what our language representations represent.

We generate distance matrices A ρ , where each entry a i , j represents the ρ-similarity between the i th and j th languages, using the three similarity measures outlined in §4 . Then, the entries in A gen contain pairwise genetic distances, computed by summing the weights of all edges on the shortest path between two leaves (languages). Similarly, the entries in A geo contain the geographical distance between countries associated with the languages. Lastly, the entries in A struct contain the cosine distance between the language representations, which are encoded in 8,607-dimensional normalized vectors.

Figure 4 shows the Spearman correlation coefficients between each pair of these matrices. The strongest correlations can be found between the language embeddings, showing that they have similar representations. The correlations between our three distance measures are also considerable (e.g., between geographical and structural distances). This is expected, as languages that are close to one another geographically tend to be similar due to language contact, and potentially shared origins (Velupillai 2012 ).

Correlations between similarities (Genetic, Geo., and Struct.) and language representations (Raw, Func, POS, Phrase, Deprel). Significance at p < 0.001 is indicated by *.

Most interestingly, the language embedding similarities correlate the most strongly with the structural similarities, rather than the genetic similarities, thus answering RQ1 . Although previous work by Rabinovich, Ordan, and Wintner ( 2017 ) has shown that relatively faithful phylogenetic trees can be reconstructed, we have found an alternative interpretation to these results with much stronger similarities to structural similarities. This indicates that, as often is the case, although similarities between two factors can be found, this is not necessarily the factor with the highest explanatory value (Roberts and Winters 2013 ).

We further strengthen our analysis by investigating RQ2 , looking at the relationships between our variables in a Causal Network (Pearl 2009 ). We use a variant of the Inductive Causation algorithm, namely, IC* (Verma and Pearl 1992 ). It takes a distribution as input, and outputs a partially directed graph that denotes the (potentially) causal relationships found between each node in the graph. Here, the nodes represent our similarity measures and language embedding distances. The edges in the resulting graph can denote genuine causation (unidirectional edges), potential causation (dashed unidirectional edges), spurious associations (bidirectional edges), and undetermined relationships (undirected edges) (Pearl 2009 ). Running the algorithm on our distribution based on all the distance measures and language embeddings from this work yields a graph with the following properties, as visualized in Figure 5 . 4

Causal network generated by IC*.

We observe two clusters, marking associations between distance measures and language representations. Interestingly, the only link found between the clusters is an association between the structural similarities and our raw model. This further strengthens our argument, as the fact that no link is found to the genetic similarities shows that our alternative explanation has higher explanatory value, and highlights the need for controlling for more than a single linguistic factor when seeking explanations for one’s results.

We train language representations on three levels of syntactic abstraction, and explore three different explanations to what language representations represent: genetic, geographical, and structural distances. On the one hand, we extend on previous work by showing that phylogenetic trees can be reconstructed using a variety of language representations (Rabinovich, Ordan, and Wintner 2017 ). On the other, contrary to a claim of Rabinovich, Ordan, and Wintner ( 2017 ), we show that structural similarities between languages are a better predictor of language representation similarities than genetic similarities. As interest in computational typology is increasing in the NLP community (Östling 2015 ; Bjerva and Augenstein 2018 ; Gerz et al. 2018 ; Ponti et al. 2018 ), we advocate for the necessity of explaining typological findings through comparison.

This is the exact same data as used by Rabinovich, Ordan, and Wintner ( 2017 ), originating from Europarl (Koehn 2005 ).

Following Rabinovich, Ordan, and Wintner ( 2017 ), we use the same implementation of Ward’s algorithm. We use vector cosine distance rather than Euclidean distance because it is more natural for language vector representations, where the vector magnitude is not important.

Trees not depicted here can be found in the supplements: http://dx.doi.org/10.1162/coli_a_00351 .

The IC* algorithm uses pairwise correlations to find sets of conditional independencies between variables at p < 0.001, and constructs a minimal partially directed graph that is consistent with the data.

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Semantics - Meaning Representation in NLP

Logic and logical forms, the meaning of simple objects and events, quantifiers and the meaning of determiners, the meaning of modifiers, relative clauses, plurals, cardinality, and mass nouns, question-answering, how many and which, who and what, discourse referents, anaphora, definite reference (the), word sense disambiguation, ontological methods, statistical methods, logical forms and lambda calculus, semantic rules for context free grammars, prolog representation, semantics of a simple grammar, quantified noun phrases, semantics of filler-gap dependencies.

• Add to the lexicon an appropriate encoding for the determiner "a", so that it can be used in sentences like "terry wrote a program".  Hand-trace the application of the Prolog rules given in this section with this sentence and show the intermediate logical forms that lead to its logical form representation, exists(x, program(x) => wrote(terry, X)).
• Assuming the grammatical rules found in this section, find appropriate semantic representations for the following statements:
• Give an example of a yes-no question and a complement question to which the rules in the last section can apply.  For each example, show the intermediate steps in deriving the logical form for the question.  Assume there are sufficient definitions in the lexicon for common words, like "who", "did", and so forth.
• Look at program 4.2 on p 102 of Pereira.  Using a trace, show the intermediate steps in the parse of the sentence "every student wrote a program."

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Definition of representational adjective from the Oxford Advanced Learner's Dictionary

representational

• It is on pottery that representational art first appeared in ancient Greece.

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