thesis meaning in tamil language

Translation of "Thesis" into Tamil

ஆய்வுக் கட்டுரை, ஆய்வேடு, ஆய்வேடு, ஆய்கோள் are the top translations of "Thesis" into Tamil. Sample translated sentence: I left Salamanca while still doing research on my Canon Law thesis, which I presented in 1968. ↔ இருப்பினும் கிறிஸ்தவ சமயச் சட்ட ஆராய்ச்சிக் கட்டுரைக்காக ஆராய்ச்சியைத் தொடர்ந்து, 1968-ல் அதை சமர்ப்பித்தேன்.

English-Tamil dictionary

ஆய்வுக் கட்டுரை, ஆய்வேடு.

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Automatic translations of " Thesis " into Tamil

Translations with alternative spelling

A statement supported by arguments. [..]

ஆய்வுக்கட்டுரை

Less frequent translations

• ஆராய்ச்சிக் கட்டுரை
• இடுநூல் ,ஆய்வேடு, ஆய்வு நூல்
• தட்டு, கருத்து.
• முன்மொழிவு உரை
• முற்கோள்; முன்னீடு

Phrases similar to "Thesis" with translations into Tamil

• thesis, antithesis, synthesis முற்கோள், எதிர்கோள், இணைகோள்
• parts of thesis ஆய்வேட்டின் பகுதிகள்
• thesis, a doctoral முனைவர் பட்ட ஆய்வேடு
• turing’s thesis டியூரிங் கோட்பாடு
• anti thesis எதிர் உரை · எதிர்த்தட்டு. · முரணுரை
• extensionality, thesis of கூட்டுச் சொல்லாக்கவியம்
• body of the thesis ஆய்வுருவம்
• title of the thesis ஆய்வேட்டுத் தலைப்பு

Translations of "Thesis" into Tamil in sentences, translation memory

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translation and definition "thesis", tamil lexicon

• thesis meaning in tamil

thesis meaning in tamil with example

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English to Tamil Meaning of thesis - ஆய்வறிக்கை

ஆய்வறிக்கை, கட்டுரை, ஆய்வுக்கட்டுரையில், விளக்கவுரை, கலவை, புத்தகம், பொருத்துதல், சிறிய, துணியுடன்

Jean Buridan proposed a philosophical THESIS stating

Uh, we have proof that you plagiarized your THESIS .

Kripke says he plagiarized his THESIS ,

Before that, I spent four years working on my THESIS .

I had to write a THESIS , and I wasn't smart enough to do it.

Meaning and definitions of thesis, translation in Tamil language for thesis with similar and opposite words. Also find spoken pronunciation of thesis in Tamil and in English language.

What thesis means in Tamil, thesis meaning in Tamil, thesis definition, examples and pronunciation of thesis in Tamil language.

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Thesis Meaning In Tamil

எளிய எடுத்துக்காட்டுகள் மற்றும் வரையறைகளுடன் thesis இன் உண்மையான அர்த்தத்தை அறியவும்., definitions of thesis.

1 . ஒரு அறிக்கை அல்லது கோட்பாடு பராமரிக்கப்பட வேண்டும் அல்லது நிரூபிக்கப்பட வேண்டும்.

1 . a statement or theory that is put forward as a premise to be maintained or proved.

இணைச்சொற்கள்

2 . தனிப்பட்ட ஆராய்ச்சி சம்பந்தப்பட்ட ஒரு நீண்ட கட்டுரை அல்லது ஆய்வுக் கட்டுரை, ஒரு பல்கலைக்கழக பட்டத்திற்கான வேட்பாளர் எழுதியது.

2 . a long essay or dissertation involving personal research, written by a candidate for a university degree.

3 . கிரேக்க அல்லது லத்தீன் வசனத்தில் அழுத்தப்படாத எழுத்து அல்லது மெட்ரிக் அடியின் ஒரு பகுதி.

3 . an unstressed syllable or part of a metrical foot in Greek or Latin verse.

Examples of Thesis :

1 . ஒரு ஆய்வறிக்கை ஆலோசனைக் குழு.

1 . a thesis advisory committee.

2 . அவரது ஆய்வறிக்கை, பெர்சியாவில் மெட்டாபிசிக்ஸ் முன்னேற்றம், ஐரோப்பாவில் இதுவரை அறியப்படாத இஸ்லாமிய ஆன்மீகத்தின் கூறுகளை வெளிப்படுத்தியது.

2 . his thesis , the improvement of metaphysics in persia, found out a few elements of islamic spiritualism formerly unknown in europe.

3 . ஒரு முனைவர் பட்ட ஆய்வறிக்கை

3 . a doctoral thesis

4 . கணினி அறிவியலில் தனது ஆய்வறிக்கையை எழுத ரிசா ஃப்ரீ பேசிக்ஸைப் பயன்படுத்தினார்.

4 . riza used free basics to write her computer science thesis .

5 . விளக்கக் கட்டுரை ஆய்வறிக்கையை தலைப்பின் வரம்பாகக் கருதுவதை உறுதிப்படுத்திக் கொள்ளுங்கள்.

5 . Be sure to treat the expository essay thesis as a limitation of the topic.

6 . அவரது ஆய்வறிக்கை ஒரு அனீரிசம் பற்றியது.

6 . his thesis was on an aneurysm.

7 . திட்ட மேலாண்மையில் முதுகலை ஆய்வறிக்கை.

7 . msc project management thesis .

8 . முதுகலை அல்லது முனைவர் பட்ட ஆய்வறிக்கை.

8 . a master 's or doctoral thesis .

9 . ஒரு ஆய்வறிக்கை? கல்லூரி என்கிறீர்களா?

9 . a thesis ? you mean, for college?

10 . ஒரு சுருக்கத்தை எழுதுவது எப்படி.

10 . thesis how to write an abstract.

11 . ஆய்வறிக்கைகளைப் பரப்புவதற்கான பொது அமைப்பு.

11 . thesis public distribution system.

12 . 'டான்ஸ் யுவர் ஆய்வறிக்கை'க்கு ஏற்கனவே வெற்றியாளர்கள் உள்ளனர்

12 . 'Dance your thesis ' already has winners

13 . ஆய்வறிக்கையின் விளைவு, எதிர்ப்பு மற்றும் தொகுப்பு.

13 . thesis , anti- thesis , and syn thesis result.

14 . ஆனால் அரசுத் தரப்பு கொலைக் கட்டுரையைத் தேர்ந்தெடுத்தது.

14 . but the prosecution chose the murder thesis .

15 . உங்களின் அனைத்து ஆதாரங்களும் உங்கள் ஆய்வறிக்கையை ஆதரிக்கிறதா?

15 . does all of your evidence support your thesis ?

16 . இவை அனைத்தும் உங்கள் இறுதி ஆய்வறிக்கையில் ஒன்றாக வருகிறது.

16 . everything comes together in your final thesis .

17 . இருப்பினும், இது முதுகலை ஆய்வறிக்கைகளுக்கும் பயன்படுத்தப்படலாம்.

17 . however, it can also be used for msc thesis work.

18 . உங்கள் ஆய்வறிக்கையை ஆதரிக்கப் பயன்படுத்தப்படும் வாதங்கள்

18 . lines of argumentation used to support his thesis

19 . மாஸ்டர் ஆஃப் லாஸ் ஒரு சிறிய ஆய்வறிக்கை விருப்பத்தை உள்ளடக்கியது.

19 . The Master of Laws includes a Minor Thesis option.

20 . எனது சொந்த பட்டதாரி ஆய்வறிக்கையை நான் மேற்கோள் காட்டுவது மோசமான விருப்பமா?

20 . is it tacky that i cited my own grad school thesis ?

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Thesis meaning in Tamil - Learn actual meaning of Thesis with simple examples & definitions. Also you will learn Antonyms , synonyms & best example sentences. This dictionary also provide you 10 languages so you can find meaning of Thesis in Hindi, Tamil , Telugu , Bengali , Kannada , Marathi , Malayalam , Gujarati , Punjabi , Urdu.

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This paper presents an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil named Thamizhi Morph. Tamil is a low-resource language in terms of NLP processing tools and applications. In addition, most of the available tools are neither open nor extendable. A morphological analyser is a key resource for the storage and retrieval of morphophonological and morphosyntactic information, especially for morphologically rich languages, and is also useful for developing applications within Machine Translation. This paper describes how Thamizhi Morph is designed using a Finite-State Transducer (FST) and implemented using Foma. We discuss our design decisions based on the peculiarities of Tamil and its nominal and verbal paradigms. We specify a high-level meta-language to efficiently characterise the language’s inflectional morphology. We evaluate Thamizhi Morph using text from a Tamil textbook and the Tamil Universal Dependency treebank version 2.5. The evaluation and error analysis attest a very high performance level, with the identified errors being mostly due to out-of-vocabulary items, which are easily fixable. In order to foster further development, we have made our scripts, the FST models, lexicons, Meta-Morphological rules, lists of generated verbs and nouns, and test data sets freely available for others to use and extend upon.

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1 Introduction

The Web contains a large and rapidly-growing textual volume of Tamil, a Southern Dravidian language of South Asia. Footnote 1 Several organisations and individuals are working on Tamil language computing. However, in comparison to some major European languages as well as Chinese, Japanese, and Korean (CJK), not many natural language processing (NLP) tools such as Part of Speech (POS) taggers, morphological analysers or syntactic parsers are available for Tamil. In addition, many existing tools are either not open, or not extendable due to technical or license restrictions. There are also commercial products such as Google Translate and Google Optical Character Recognition. However, more work needs to be done to improve their quality before those can be put to general use. Beyond that, as claimed by Bhattacharyya et al. ( 2019 ), South Asian languages in general lack sufficient language resources in terms of gold standards or benchmark data needed for supervised machine learning.

A MAG is a useful resource for language application development and language learning. It can be used to effectively do word-level translation, especially for MRLs. Named entity translation is also a task where a MAG can be useful. For instance, in Moses , a statistical machine translation system, morphological analysers are used in its factored translation models to improve the results over non-factored models (Koehn et al. 2007 ). Further, Passban et al. ( 2018 ) found that integrating morphology into a neural machine translation pipeline is useful to help overcome out-of-vocabulary problems, especially in MRLs. Machine translation is a pressing problem in Sri Lanka, where two major languages—Sinhala and Tamil—are in everyday use, including their use in official government communications.

We have used Thamizhi Morph to aid our grammar development within the ParGram project Footnote 3 (Butt and King 2002 ) to allow for the possibility to work with stems in the syntactic parser and generator, rather than full-form lexical entries.

A MAG is also useful for language learning purposes so that a learner can analyse and search for morphs of an inflected word. Our morphological analyser is designed to give the morphs in addition to the morphemes. This is important for languages with complex morphology as well as irregular words, where it is difficult for a learner to identify root words (Seiss 2012 ).

2 Background

2.1 the tamil language.

Tamil is spoken natively by more than 80 million people across the world. It has been recognised as a classical language by the government of India since it has more than 2000 years of continuous and unbroken literary tradition (Hart 2000 ). It is one of the official languages of Sri Lanka and Singapore, and has regional official status in Tamil Nadu and Pondicherry, India. It has also been recognised as a minority or indigenous language in several countries including Malaysia, Mauritius, and South Africa, and is taught there as a second language. The spoken forms of Tamil vary depending on the region, mainly due to language contact, and/or politics (Schiffman 2008 ). Within Sri Lanka, there are several varieties of Tamil that are spoken, and at times, one speaker may not understand the other. Further, there is no common agreement among the different governments on the choice of terminologies. Consequently, terminological variation also exists.

2.2 Finite-State morphology

In the conception of two-level morphology, a word is represented at two levels, namely the lexical level, or lexical form, and the surface level, or surface form. This concept can be modelled computationally using Finite-State Transducers (FST) (Beesley and Karttunen 2003 ) and is referred to as Finite-State Morphology (FSM). A FSM approach has been widely used to develop successful early applications for morphologically rich languages such as Finnish and Russian (Koskenniemi 1983 ; Karttunen and Beesley 2001 ). Subsequently, it has been taken up by researchers developing morphological analysers for other languages, including South Asian languages such as Urdu (Bögel et al. 2007 ), Sindhi (Rahman 2016 ) and Nepali (Prasain 2011 ), and also for the morphologically extremely complex Australian language Murrinh-patha (Seiss 2012 ).

Several tools have been developed to model FSM. Proprietary tools like the Xerox Finite-State Transducer (XFST) (Beesley and Karttunen 2003 ), and the FSM Library from AT&T (now in OpenFST) have been widely used in the past. Open source solutions like OpenFST (Allauzen et al. 2007 ), HFST (Lindén et al. 2009 ) and Foma (Hulden 2009 ) are also employed. XFST has been used widely as an aid to grammar engineering in the LFG/XLE context (Beesley and Karttunen 2003 ; Butt et al. 1999 ; Rahman 2016 ) as part of the ParGram effort.

Foma is a C library and a compiler that is used to develop FSTs for various purposes, including the development of language applications such as a MAG. In this section, we briefly discuss how Foma can be used to model inflectional morphology.

Developing an FST-based morphological analyser generally requires two components: (1) list of morphs (morphotactics and lexicon); (2) alteration rules (morphophonological rules and orthographical rules) (Beesley and Karttunen 2003 ; Hulden 2009 ). Foma also has these two components. A lexicon component shows the ordering restrictions of the root and its morphs, and maps them to an intermediate or final form. For instance, (1) (a) shows how a plural morpheme can be mapped to intermediate and final forms respectively, where the morph s is used to mark the morphemes +Noun and +Pl . However, as shown in (1)(b), it does not yield the final results for all constructions. This is where the second component, alteration rules, comes in. In this component, we define the morphophonological or orthographic changes which take place. In other words, as a first step, we consider the generic scenario, where root forms take s to mark plural, and then write alternation rules for the exceptional cases, as in (2). In Foma we can define such a rule, as in (2), where we define, for example, an eInsertion rule for plural constructions and add a letter e in front of plural morph s whenever a noun ends with the letters ch . In this way we can generate the final plural form for watch , as watches . We can define any number of such rules and can then concatenate them and apply them to the output of the lexicon component to get the final forms.

The words of a language can be divided into different classes based on their morphophonological behavior. When we develop a morphological analyser we therefore need to define different lexicon components for each of the identifiable word classes. Each of these lexicon components is associated with different sets of alteration rules. This feature is useful for a MRL such as Tamil, where the alteration rules are complex. In Foma we can define these classes as different lexc files that can then be concatenated as necessary integrating alteration rules.

3 Tamil morphology

In this section, we provide some basic information about Tamil morphology and then discuss the paradigms we have used in order to develop the analyser.

The Tamil grammar tradition classifies words in Tamil as either divisible or indivisible. A divisible word can have up to six parts, namely: root, suffix, idainilai (medial particle), chariyai , Sandhi , and vikaram (alteration) (Nuhman 1999 ; Senavaraiyar 1938 ). The medial particles can be tense markers or negation markers in divisible verbs as shown in (3). chariyai is a phonological modifier which can be further divided into a euphonic marker and an oblique marker based on the function it expresses (Lehmann 1993 ). The term Sandhi refers to a cluster of morphophonological phenomena, the alteration is also a morphophonological process of assimilation which has orthographic consequences.

The six parts of a Tamil word can be illustrated using the following example in (4), which is taken from Sarveswaran and Butt ( 2019 , p. 274). Here, an internal Sandhi t occurs between the root and the tense particle t , and then it becomes n because of vikaram (alternation).

Word formation through agglutination exhibits fusional tendencies or morphophonemic alterations between the root word and grammatical formatives. Based on these alterations, several different conjugational and declinational patterns or paradigms for verbs and nouns, respectively, can be identified. In Sects.  3.2 and  3.3 we describe verbal and nominal paradigms that have been considered in order to be able to develop the morphological analyser.

3.1 Part of speech

In grammar books written by native grammarians (Thesikar 1957 ; Senavaraiyar 1938 ) Tamil words have been primarily divided into four types, namely: nouns, verbs- intensifiers/attributives, and particles. However, more recently there have been more granular Part of Speech (POS) analyses proposed by Sarveswaran and Mahesan ( 2014 ); Baskaran et al. ( 2008 ); Lehmann ( 1993 ). We follow Lehmann ( 1993 ) and Sarveswaran and Mahesan ( 2014 ) closely. These are relatively less granular when compared to others, but we have found that these allow for the most accurate analysis in our implementation.

3.2 Verbal morphology

In addition to simple verbs, Tamil also has complex or compound verbs that have more than one verbal root within them, which may express mood, aspect, negation, interrogativity, emphasis, speaker perspective, and conditional and causal relations (Annamalai et al. 2014 ). Agesthialingom ( 1971 ) claims that Tamil can have up to four verbal roots in one verb. For instance, there are four verbal roots in the complex verb in (5): vaa (come), koḷ (hold), iru (be) and iru (be). The koḷ (hold) and iru (be) in the middle together signal continuous aspect. Further, as shown in the example in (5), in verbal conjugation, only the last verb in the sequence takes tenses and person, number and gender (png) marking. All the preceding verbs appear either in a participial form or an infinitival form.

A complex verb in Tamil can be written as two tokens, as in (6), or as a single token as in (7). If a complex verb is written as one word as in (7), then the analyser should provide a proper analysis, including an identification of all the verbal roots in it.

As a step towards this goal, we have identified a set of verbs (Boologarambai 1986 ) which form complex verbs together with the main verb. We have categorised these according to their structure and function, based on discussions in Boologarambai ( 1986 ), and our study, as shown in Table  1 .

3.2.1 Verbal paradigm

3.2.2 verbal conjugational forms.

Annamalai et al. ( 2014 ) have identified 254 forms for each Tamil verb after a rigorous analysis of their corpus of contemporary texts. Some verbs may not take all of the 254 forms. Rajaram ( 1986 ) has identified 21 forms for each verb from a pedagogical perspective. On the other hand, Kumar et al. ( 2010b ) claim that a Tamil verb lemma can take up to 8,000 forms although not all are listed or found in the literature. In our Thamizhi Morph we have implemented 260 inflectional forms. These forms are the set common to Annamalai et al. ( 2014 ) and Rajaram ( 1986 ). For each lemma, these 260 forms are generated and analysed. However, more forms can easily be added to the system without the need for any additional programming using our Meta-Morph rules (Sarveswaran et al. 2019 ).

3.3 Nominal morphology

Traditional grammarians have identified 8 cases including a vocative (Senavaraiyar 1938 ; Thesikar 1957 ). However, modern linguists (Nuhman 1999 ; Paramasivam 2011 ; Lehmann 1993 ) argue that the instrumental case proposed in traditional grammar should be treated as two, namely instrumental and sociative. In our analyser, we adapted this modern classification, and the 9 cases are shown in Table  3 . This table also shows some example case suffixes or markers which we have handled in our analyser. Footnote 6

The rationality and gender of nouns are important information that will be also shown in the analysis, because this is valuable information for syntactic and semantic processing.

Tamil does not have a definite marker. The definiteness of nouns is expressed with demonstrative markers, or by using the accusative case marker in irrational objects (Lehmann 1993 ). The object of a sentence is marked by accusative case, however, the accusative case marking is compulsory only for rational objects (Lehmann 1993 ; Nuhman 1999 ). Therefore, when an irrational noun has an accusative marker, it is also marked for definiteness.

3.3.1 Nominal paradigm

Rajendran ( 2009 ) has proposed a paradigm for noun morphology with 26 classes based on their morphophonological properties. Among these 26 classes, 9 classes are used to capture the morphophonological rules pertaining to pronouns. Pronouns take different forms when inflecting for a case suffix.

In our noun paradigms, we have identified 38 classes for pronouns that include personal, possessive, and interrogative pronouns. We found that although many pronouns are subject to the same morphophonological rules, they produce different analyses or lexical strings. Therefore, these have been sorted into different classes.

3.3.2 Nominal conjugational forms

We used 36 conjugational forms for Tamil nouns. These cover plural and case conjugations, along with external Sandhi markers. Each noun root takes case markers both in its singular form and plural form. Further, nouns in their dative or accusative forms can also take one of four external Sandhi markers. Altogether, we have 36 nominal conjugational forms. It is common to suffix postpositions to nouns. We are in the process of also including such constructions as part of Thamizhi Morph.

4 Related work

4.1 approaches for developing morphological analysers.

There have been rule-based, machine learning and deep learning approaches proposed and developed for morphological analysers of various languages, including Dravidian ones. However, except for Premjith et al. ( 2018 ) for Malayalam, no deep learning-based attempts have been taken in the development of a morphological analyser for any Dravidian languages. This may be due to inadequate data available to train a deep learner, as stated in Bhattacharyya et al. ( 2019 ). On the other hand, rule-based approaches yield immediate and high quality analyses and have therefore been widely employed for similar languages.

4.2 Morphological analysers for South Asian languages

A number of studies have been done on FSMs for South Asian languages. One of the earliest was Bögel et al. ( 2007 ) for Urdu, which includes a transliteration component so that the morphological analyzer and generator can also be used for the structurally almost identical language, Hindi. In addition to inflectional and derivational morphology, it also tackles complex problems such as reduplication and compounding. Prasain ( 2011 ) has developed an FSM for Nepali. He has identified different classes of nouns, pronouns, verbs, adjectives, numerals, adverbs, conjunctions, postpositions, and particles for which he then implemented a pilot morphological analyser and generator using the two-level morphology approach and XFST tool. Rahman ( 2016 ) has developed an analyser and generator for Sindhi as part of his work on a grammar development for Sindhi. He also used XFST, which he then integrated within his grammar.

4.3 Tamil morphological analysers

Antony and Soman ( 2012 ) carried out a survey on the state of affairs of computational morphology across Indian languages, and documented 17 efforts of morphological analysers and/or generators for Tamil. 12 of them were carried out before 2007, and the relevant papers, data sets and/or software are not retrievable via the Internet. The rest have been carried out since 2010. Among those five efforts, Kumar et al. ( 2010a , b ) and Menaka et al. ( 2010 ) are available for download in binary form yet without any data sets.

Menaka et al. ( 2010 ) and Kumar et al. ( 2010a ) have used rule-based approaches which only perform morphological generation. On the other hand, Kumar et al. ( 2010b ) used machine learning for the morphological analysis and generation of Tamil. They claim that the system was tested using 40,000 verbs and 30,000 nouns, and that the machine learning system was trained using 130,000 verbs and 70,000 nouns from their corpus. However, neither data sets, sources nor any detailed documentation are available, except for a sample corpus with 270,000 tokens. The extendability of this work to aid grammar development is also questionable, and would need to be researched. An email exchange with the authors has established that they do not work in this domain anymore.

Parameshwari ( 2011 ) has implemented a morphological analyser and generator for Tamil using a rule-based approach, which covers verbs, nouns, adjectives, pronouns, numerals and non-standard Tamil words with the use of the Apertium tool. The author claims that the system shows an accuracy of  84%. Only the research publication could be retrieved, and there are no associated data sets or rules available in the paper, or online. Lushanthan et al. ( 2014 ) have proposed a morphological analyser and generator for Tamil, which has been implemented using XFST. The authors have used transliteration to handle the Tamil script, given that the current version of XFST has rendering issues, although it supports Unicode internally. The authors have considered 2,000 noun and 96 verb stems as part of their analysis and generation. They have tested the system using their own data set consisting of 3,500 nouns and 500 verbs with a success rate of 78%. However, the data sets and XFST rules have not been made available.

Anna University in India developed a morphological analyser in 2001 called Atcharam that has recently been added to the GitHub repository. Footnote 7 It was developed for TAB (TAmil Bilingual) encoded text as a stand-alone application using Java. There is, however, no detailed technical documentation or rule set, although some data in the form of a list of words are available in the repository. These are encoded using TAB, and an attempt to convert them to Unicode was also not successful. There are also some morphological tools available in the Github code repository without corresponding academic publications.

There is a Tamil Shallow Parser, Footnote 10 published in 2009, which also provides morphological information in addition to POS and Chunking. However, there are no papers which cover the development of the Tamil shallow parser. The authors of this tool have developed similar tools for Hindi, Telugu and Bengali, as documented in Avinesh and Karthik ( 2007 ). The authors report results for POS and shallow parsing, but not for the morphological analysis. It is not clear what approach has been used, particularly for the morphological analysis as this is not discussed or evaluated. The analysis of this tool comes in a custom version of Shakti Standard Format (SSF) (Bharati et al. 2007 ), which is not detailed in any of the literature found online. The original tool which was published in 2009 is also available for download. The data and rules in this tool are encrypted. However, we were not successful in executing it. The first author of this tool has now re-implemented it using Python. Footnote 11 However, the new version no longer includes morphological parsing. We therefore used the online demo to do testing, and the results are reported under the evaluation section.

There has also been an attempt to develop a Morphological analyser for Tamil using a support vector machine (Mokanarangan et al. 2016 ). Although the writers have reported an accuracy of 98.73%, their system is not available, and it is not clear what data sets or analyses have been used for training and testing, as there is no data available in the paper, or online.

5 Design choices

Our research on existing Tamil morphological analysers has shown that existing analysers either could not be not found, are incomplete, or not maintained. All of these tools are dependent on versions of various programming languages, and the logic seems directly coded in that particular programming language. This makes these tools difficult to maintain, test or extend. Also, some of these applications process text in ASCII (i.e., transliterated) format, and do not support Unicode encoding. Since Unicode has now become the de-facto method of encoding text, all applications are expected to have Unicode support. Further, we are in the process of developing a ParGram style computational grammar for Tamil, which requires a morphological analyser with a good, precision coverage, implemented with the use of a finite-state approach that interfaces with the grammar.

Therefore, we decided to develop a Finite-State transducer based morphological analyser. We wanted to make sure that our tool is technology, or programming language neutral so that it can be accessed via any programming language without wrapping it with an API (Applications Programming Interfaces). We also wanted it to be light-weight, so that it could be run on any commodity hardware, and be open source so that anyone can take it and extend it as needed or desired.

5.1 Technology stack

The application of Deep Learning in almost every NLP task has become common in the computational world. However, it has not yet become the state of the art for morphological analysis. It is essentially the lack of sufficient quality data that is the bottleneck for the application of deep learning approaches (Marcus 2018 ) and most Indic languages, including Tamil, do not have sufficient annotated data needed for supervised machine learning.

On the other hand, Finite-State Transducers (FST) have shown proven success in the past for morphologically rich South Asian languages as discussed in Sect.  4 . Moreover, the development of a computational grammar using Lexical Functional Grammar and XLE for the ParGram project (Butt and King 2002 ) is also in our project pipeline, which requires a Finite-State morphological analyser. Therefore, it was decided to use a FST. In addition to morphological analysis, FST can also be used for morphological generation; this is an added advantage.

There are several currently available tools with which to implement an FST-based morphological analyser. These include XFST, OpenFST, HFST, and Foma. Among these, XFST has been the standard tool for developing morphological analysers. This is because an XFST-based morphological analyser can easily be integrated into a computational grammar built using XLE. However, XFST has limited support for Unicode characters, especially for complex ones like Tamil. Additionally, it is a closed source, and proprietary tool. Foma, on the other hand, has support for Unicode, and is an open-source software that can be easily extended to web applications. For these reasons it was decided to use Foma to implement our morphological analyser.

5.2 Scope of annotations

We decided to capture and encode all available information that words express via their form. Apart from POS and morphological information, we have therefore also represented morphophonological information.

Apart from the morphological features which are carried by morphs in a word, we have also included the lexically specified features shown in Table  5 in our analysis. Since it is not always possible to extract these features orthographically, we thought that such information should be included additionally in the analysis, as these are useful for developing applications or resources such as POS taggers and computational grammars.

5.3 Morpheme labels

We use our own labelling scheme in our morphological analyser though there is an effort to harmonise morpheme labels across languages and tools, especially with a cross-lingual morphological transfer in mind, for example, the UniMorph project (Kirov et al. 2016 ). However, UniMorph does not capture all our required concepts, such as rationality of nouns, and the strong/weak nature of verbs, which are necessary for the morphological processing of Tamil. In addition, since we have developed this analyser having grammar engineering in mind, it is always good to mark the morpheme information of a single morph together. For instance, person, number, gender and rationality of a given noun can be marked by a single morph in Tamil. It is thus easier to mark it as a single morpheme in order to reduce the complexities in modelling. In contrast, the UniMorph project proposes separate labelling for all of this morphological information. Taking into account all of this, it was decided to design a transparent scheme for our morpheme labels, which can then be mapped to other annotation schemes as required.

Analyses of each word are given the following form in our system:

Apart from the morpheme information, the morph which corresponds to the morpheme is also recorded in the analysis for future use. Additionally, each morpheme is separated using a morpheme boundary ’|’, similar to what is used by Beesley and Karttunen ( 2003 ) to mark term boundaries (they use ‘TB’). We made our choice in part because “|” is the symbol used in Universal Dependencies (UD) to separate features there. Footnote 12

6 Thamizhi Morph

Based on the design choices outlined in the previous section, we have developed a morphological analyser and generator for Tamil using a Finite-State approach with the aid of Foma. This section outlines the architecture of our tool, including the pre-processing steps, data gathering approaches, compilation of rules, and the development of the tool.

6.1 Pre-processing

6.2 compilation of lexicons.

Lexicons for Tamil verbs, nouns, and other particles have been compiled from various sources as outlined below via books, a dictionary, and corpora. The words were then classified on the basis of the paradigms outlined in Sects.  3.2 and  3.3 . Adjectives, adverbs, and other particles such as conjunctions have been compiled as separate lists.

6.2.1 A lexicon of verbs

A lexicon of 3300 lemmata of Tamil verbs have been compiled from the following two verified sources:

Ramakrishnan ( 2014 ) has identified 369 of the most frequently used verbs in Modern Tamil. This analysis is based on a corpus of 7 million tokens compiled from the web and has taken into account expert advice on linguistic matters. This list has been included in the contemporary Tamil dictionary Cre-A (Ramakrishnan 2014 ).

Irākavaiyaṅkār ( 1958 ) surveyed the Tamil classic literature up until 1958, where he identified 3124 lemmas, and categorised these into 12 classes as per the classification proposed by Graul ( 1855 ) and Sithiraputhiran ( 2004 ). However, some of these forms are not used in the contemporary language. Nevertheless, since the analysis of these verbs is necessary in order to process historical Tamil texts, the entire list has been used for the development of our FSM.

In addition, a lexicon of complex verbs has been manually constructed by joining the infinitival form or verbal participial form of verbal roots, together with secondary verbs (Boologarambai 1986 ) as identified in Sect.  3.2 and in Table  1 . For instance, all the modal complex verb constructions are done by joining the infinitival form of the verb together with a modal auxiliary verb, as in (11) (a). Similarly, aspectual markers are always joined with a verbal participial form of the root verb, as in (11) (b).

6.2.2 A lexicon of nouns

6.3 meta-morph rules.

We integrate the novel concept of Meta-Morph Rules that we developed and presented in Sarveswaran et al. ( 2019 ). Meta-Morph Rules are lexical rules in the form of metadata that is fed into the development of our Foma morphological analyser. As discussed above as part of the review of Tamil morphological analysers, most of the previous efforts at encoding the morphotactics of Tamil have been deeply coupled with a particular programming logic. Other efforts have relied on heavy manual effort.

The definition and use of Meta-Morph rules help us to focus upon the analysis of the language without the distraction of being bound by a particular programming logic. It additionally allows for the automation of the generation of lexical entries, which, when done manually, is not only a tedious and time-consuming task, but also prone to error. This is particularly true for a language like Tamil where each verb may display several hundred inflections. Therefore, even if a paradigm approach is used, it is challenging to write rules, maintain them and perform regression testing without the aid of a meta-grammar.

We initially developed our MAG by entering all of the necessary lexical strings manually, which is a tedious task that took time and energy. However, this manual process helped us to understand the overall generalised morphological structure of Tamil. In evaluating our progress, we found that correcting errors was complicated and time-consuming, since we always had to engage with the details of the Foma specifications. The frustration with these time-consuming tasks led us to experiment with Meta-Morph Rules.

The idea was to find a way of stating the morphotactics needed to analyse and generate Tamil words in a manner that would be transparent, programming language independent and easy to maintain. As shown in Snippet-1, we hit upon a format that contains the following information: (1) The word classes to which the information applies (Line 1); (2) the inherent lexical specifications for that word, for instance, the classes here can be finite, simple, and indicative (Line 2); (3) the order of the morphemes (Line 3); (4) particular patterns, for example, as in Line 4 where it is stated that verbs (of the classes defined in Line 1) which contain euphonic markers (the material used to fulfill phonological phrasing requirements) are constructed only with past tense verbs, and only with a specific png marker (e.g., pngeuph in Line 4).

We found the writing of rules in the Meta-Morph format illustrated by Snippet-1 to be quick, easy and transparent. In terms of translating these descriptive statements into an implementation, we found that defining feature-value pairs using JSON Footnote 13 to be the most efficient way forward. Adding in the extra step of formuiating Meta-Morph Rules coupled with the JSON knowledge base helped us to significantly accelerate the process of developing our FSM for Tamil. Adding a lexical string or new conjugation form now becomes very straightforward: All that is required is to list the classes which will take those new forms and then define a generalised rule for the formation of that word, as shown in Snippet-1. A complete set of Meta-Morph Rules for finite and indicative verbs are shown in Appendix A: Snippet-4. An overview of all the system components and processes is shown in Fig.  1 .

The process outline: shows how actual FST is build from Meta-Morph Rules and other components

The JSON files contain detailed morphophonological and orthographic information about the values for the labels in the Meta-Morph Rules. As shown in Snippet-2, data are stored in the JSON files as key-value pairs which are also human-readable. In addition to labels, values corresponding to each morph are also stored in the JSON files, as shown in Snippet-2. For instance, tense is defined as consisting of the values past, future (fut) and present (pres) and these values can themselves be further specified, as demonstrated for past tense, where two different possibilities are provided. This information becomes part of the lexical analysis. This data structure provides desirable flexibility for defining different tense markers and labels for different classes, and these data can be referred to at different levels when writing the Meta-Morph Rules. For instance, as shown in Snippet-1, both the tense feature or the past feature can be referred to independently from one another. Furthermore, in case there was a mistake in the labelling or in the specification of the value of a marker, corrections can now easily be done directly in the descriptive but hierarchical JSON text file without needing to engage with the details of the FSM programming logic.

The above rules and JSON entries can be written in a plain text file. For instance, Snippet-2 shows how tense labels are defined and stored in a JSON file. As shown here, there can be different past tense markers for different classes of verbs. For general cases, the tense marking can be done as shown in line 3. However, if required, a particular tense marker can also be used, as shown in line number 4 of the above Snippet-1.

Once the Meta-Morph Rules are finalised, they can be parsed to produce actual lexical strings that are then fed to Foma to compile an FST. A parser has been developed using Python to parse these Meta-Morph Rules to generate lexical rules for Foma. A sample of a compiled Meta-Morph Rule is shown in Snippet-3. As mentioned previously, we use the pipe “|” symbol to mark morpheme boundaries. The % in Snippet-3 is used to allow us to escape special characters in the lexical string.

Apart from the generation of these intermediate entries, orthographical rules have been written for each class in the paradigm, as necessary, based on the description in Sect.  6.4 . If a new class needs to be introduced, then a new set of entries needs to be added to the orthographical file. Otherwise, there is no need to touch the lexical strings or the orthographical files.

6.4 Orthographical rules

Writing orthographic rules is a complex task for a language like Tamil. In most cases, the affixation of suffixes is not just a mere addition to a lemma. Rather, several orthographical changes take place during the affixation process in Tamil, due to grammatical and phonological reasons. These complicate the process of writing orthographic rules. The following are common orthographical changes observed when suffixation occurs. These have been programmed to be handled by our analyser and generator:

6.5 Morphological guesser

We have also implemented a guesser to analyse nouns, verbs, adjectives, and adverbs that are not part of our lexicons. We identified common suffixes along with corresponding analyse to develop this guesser. If the guesser does not match any suffixes which we have listed, then it will recognise the word as a noun with a nominative case. This is a useful component to tackle out-of-vocabulary problems.

7 Evaluation

There are currently no benchmark data sets available for Tamil to evaluate language processing applications, including morphological analysers. Researchers tend to use their own data sets to evaluate and report results. In Sarveswaran et al. ( 2019 ) we reported an evaluation that we did using a POS tagged corpus found online. In that evaluation, we checked whether a given word in the corpus could be analysed using our analyser. This evaluation was useful for us to check the overall coverage. In this paper, we report on two experiments. In the first instance, we have taken text from an elementary Tamil text book that is part of the Sri Lankan school curriculum. This contains 612 unique words, and comprises words with a good sample coverage of different types of POS, compound words, and foreign words. We have conducted a comparative evaluation of our Thamizhi Morph and the IIIT’s Tamil shallow parser with respect to this data set. Secondly, we have used a Tamil text from UD v2.5 to do a detailed error analysis.

7.1 Comparing Thamizhi Morph and IIIT Parser

A sample output from the IIIT shallow parser

A sample output from Thamizhi Morph

We passed the 612 words taken from the elementary school Tamil text to IIIT shallow parser, using our script, and to Thamizhi Morph. The results are shown in Table  6 . As shown, out of 612 words, IIIT parser analysed 585 words and Thamizhi Morph analysed 571 words. Table  6 also shows how many of those analyses are correct analyses, and what percentage of lemmas are correctly predicted. While Thamizhi Morph gives always correct analysis, it failed to guess lemma associated with 12 words. This is due to the complex nature of the Tamil writing system. However, this can be corrected by adding more alternation rules.

The errors found with Thamizhi Morph are instances either of out-of-vocabulary items or of derivational formations that have not as yet been included in the FSM, yielding items that are unknown to the analyser. The out-of-vocabulary errors are easily remedied, as we can simply add the missing vocabulary to our lexicon table. The derivational formations are more complex and we have begun adding derivations on a case by case basis. We do note that the initial focus of our work is the development of a morphological analyser for Tamil inflectional, not derivational morphology. As such it is not surprising that Thamizhi Morph cannot as yet analyse instances of derivational morphology. However, we do have the flexibility to now proceed with necessary corrections and extensions.

7.2 Evaluating Thamizhi Morph using UD Tamil Treebank

In the second experiment, we used the text in the available Tamil Universal Dependency Treebank v2.5 to evaluate Thamizhi Morph. The treebank, in total, consists of 8,635 tokens from 600 sentences. There are 3,567 unique words prior to tokenisation, and this is increased to 4,055 tokens after multi-word tokenisation. Because there are inaccuracies in the multi-word annotations, and the UD annotations, we decided to work with the list before tokenisation, i.e. with the 3,567 unique words. Of these, Thamizhi Morph successfully analysed 3,023 words but failed to analyse 544 words, that is 84.7% of the words have been successfully analysed. The following are the reasons for the errors:

In working with the UD Tamil treebank, we identified numerous annotation problems with the treebank. One necessary adjustment is the need of extending the current UD morphological labels to reflect all of the actual morphological information we have in Tamil. This can be done via the language-specific features proposed in the UD guidelines themselves. Footnote 15 For example, we found that the current version of the UD morphological feature inventory does not have labels to mark rationality, euphonic markers, and Sandhi effects.

We have also developed a tool to populate the Thamizhi Morph morphological annotations to the CoNLL-U format Footnote 16 which is used in UD treebanks annotation as well. We believe that our extension could be a useful resource for the creators of Tamil UD treebank.

8 Conclusion

In this paper, we described the design and performance of a Tamil Morphological Analyser cum Generator, Thamizhi Morph. Tamil continues to be a low-resource language in terms of the processing tools/applications available for others to use and extend. We have contributed to ameliorating this situation by developing a set of resources, including lexicons of verbs and nouns, Meta-Morph rules, and a list of 1M words which are generated from Thamizhi Morph that are all available for others to use and extend. Footnote 17 , Footnote 18 The FST models published are programming language independent resources that can further be used for language processing applications. We are currently mainly using them in the context of Tamil grammar development, but we are seeking to also integrate them into the development of machine translation applications (Ranathunga et al. 2018 ) and spell checkers (Uthayamoorthy et al. 2019 ). Since we have also made our rules and lexicons openly available, our work can be easily extended to other similar languages. The Meta-Morph Rules which we have published are simple to modify and to extend and can be used as a basis for the development of a morphological analyser for other Dravidian languages.

Although no benchmark resources exist for an evaluation of NLP applications developed for Tamil, we designed two evaluation experiments to test the coverage and accuracy of Thamizhi Morph. The results are very good in that identified errors are either due to out-of-vocabulary items or derivational formations that have not as yet been implemented.

In future work, since we can generate a large amount of morphologically parsed data using Thamizhi Morph, we can perform several experiments. In particular we will experiment how morphological embedding will perform compared to Byte-Pair Encoding (BPE) in the context of Neural Machine Translation (NMT) specially in the context of Tamil translation. We will also explore the possibilities of using the current rule-based inflectional morphological analyser to develop a deep learning based analyser for both inflectional and derivational morphology. Further, the current analyser gives us all possible analyses for a given surface form. As a next step, we will also develop a contextual morphological analyser by merging Thamizhi Morph and a Part of Speech tagger. In addition, we intend to explore whether the Meta-Morph Rule interface can be further generalised and used for other South Asian Languages. We also create a benchmark data set with quality data as part of our future work. We have furthermore identified the need for more in-depth linguistic studies of verbal constructions, especially complex verbal predication so as to identify and implement the right approach in Thamizhi Morph.

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Acknowledgements

We would like to thank Lauri Karttunen from Stanford University, Rajendran Sankaravelayuthan from Amrita University, and Mans Hulden from the University of Colorado Boulder for their thoughts and technical support in making this work possible. We would also like express our appreciation to Maris Camilleri from the University of Essex for her support in language editing, and two anonymous reviewers for their valuable comments and inputs to improve this menu script. This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education, Sri Lanka funded by the World Bank, and also supported by the DAAD (German Academic Exchange Office).

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Appendix A: A sample meta-morph rules for finite verbs

Appendix B: Screen capture of an analysis from Thamizhi Morph

Thamizhi Morph: Screen capture of an analysis

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Sarveswaran, K., Dias, G. & Butt, M. Thamizhi Morph: A morphological parser for the Tamil language. Machine Translation 35 , 37–70 (2021). https://doi.org/10.1007/s10590-021-09261-5

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