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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

பேச்சின் வேகம்

உரை மொழிபெயர்ப்பு, மொழிபெயர்ப்பு முடிவுகள், ஆவண மொழிபெயர்ப்பு, இழுத்து விடவும்.

இணையதள மொழிபெயர்ப்பு

URLலை உள்ளிடுக

பட மொழிபெயர்ப்பு

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Translation of thesis – English–Tamil dictionary

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• I wrote my thesis on literacy strategies for boys .
• Her main thesis is that children need a lot of verbal stimulation.

( thesis 在 Cambridge English–Tamil Dictionary 的翻译 © Cambridge University Press)

thesis 的 例句

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a group of four people who play musical instruments or sing as a group

Alike and analogous (Talking about similarities, Part 1)

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ஆய்வறிக்கை  (Tamil) Translated to English as thesis

ஆய்வறிக்கை in more languages.

• in Kannada ಪ್ರಬಂಧ
• in Telugu థీసిస్
• in Malayalam തിസിസ്

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Thamizhi Morph: A morphological parser for the Tamil language

• Open access
• Published: 23 April 2021
• Volume 35 , pages 37–70, ( 2021 )

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• Kengatharaiyer Sarveswaran   ORCID: orcid.org/0000-0003-1579-0597 1 ,
• Gihan Dias 1 &
• Miriam Butt 2  

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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.

meta.wikimedia.org/wiki/Growing_Local_Language_Content_on_Wikipedia_(Project_Tiger_2.0)/Writing_Contest.

Thamizhi is an ancient writing system that was used to write the Tamil language.

The Parallel Grammar (ParGram) Project (Butt et al. 1999 ; Butt and King 2002 ) aims to develop and implement large and wide coverage grammars for languages of different families. These parallel grammars are written collaboratively within the linguistic framework of Lexical Funcational Grammar (LFG) and with an agreed set of grammatical features by the project group members. The Xerox Linguistic Environment (XLE) (Crouch et al. 2017 ), which is a parsing and generation implementation of LFG developed at PARC, is used as a grammar development platform. In addition to putting effort into feature standardisation, the project also promotes similar analyses for similar phenomena across languages (Butt and King 2002 ), a property that is useful for cross-lingual language applications such as machine translation and information retrieval.

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https://unicode.org/charts/PDF/U0B80.pdf .

Abbreviations used in examples: vpart =Verbal Participle; inf =Infinitive; 3sn =3rd Person Singular Neuter; 1s = 1st Person, Singular; 3smr =3rd Person, Singular, Masculine and Rational; pass =Passive; san =Sandhi; rp = Relative Participle; imp =Imperative; caus =Causative; 3se = 3rd person, singular and epicene, 3seh =3rd person, singular, epicene and honorific; pl =Plural; obl =Oblique; euph =Euphonic; nom =Nominative; dat =Dative; acc =Accusative; inst =Instrumental.

Thesikar ( 1957 ) lists 28 locative markers and 10 vocative markers that are rarely used in the present context, therefore, we have not included them in this version of 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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• યુનિવર્સિટીની ઉપાધિ મેળ વવા માટે લખેલો પ્રબંધ. ⇄ thesis gujarati
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Lessons from the translation of a Historical novel in Tamil into English

The writer Kalki is known for writing historically based novels in Tamil. Some of them are Sivakamiyin Sabadam, Partipan Kanavu, and Ponniyin Selvan. Kalki Krishnamurthy’s Ponniyin Selvan is a masterpiece that has enthralled generations of Tamil readers. Many authors have written phenomenal books in Tamil literature after Kalki Krishnamurthy, but Ponniyin Selvan remains the most popular, widely-read novel. It has just the right mixture of all things that makes an epic – political intrigue, conspiracy, betrayal, huge dollops of romance, infidelity, seduction, passion, alluring women, unrequited love, sacrifice and pure love. The Part first of Ponniyin Selvan is translated into English by Indra Neelameggham in 1990. Another English version of Kalki’s Ponniyin selvan has been rendered by H.Subhalakshmi Narayanan in 2016. The present study is based on Indra Neelamggham’s English version of Ponniyin Selvan. The paper is not intended to evaluate the translation but to understand the strategies adopted by the translator for the successful translation of the historic novel Ponniyin Selvan in Tamil into English. A legendary piece of literature, Ponniyin Selvan ‘Ponni’s Beloved’ is Part One of five parts of the English translation of Kalki Krishnamurthy’s Tamil Classic ‘Ponniyin Selvan.’ It has as its protagonist Vandiya Devan a young soldier who belongs to the brave warrior race of Vanars. He is on a secret mission for the Crown Prince of the Chola Kingdom. What follows is an adventurous journey through the realms of the Chola Kingdom. Spiced with bits of gossip, fun- frolic and subtle romance, the story is actually that of deceit and dare. While we see that the thirst for power of a few, combined with the determination to wreak vengeance by some others, is ready to hit at the very foundation of a great empire, we also see the grit, determination and intelligence of one a woman, try match up to the conspiracies of the enemies in an attempt to thwart their mission and secure the future of the empire. From the point of view standards of translation explained in the beginning an attempt has been made here to understand the strategies adopted by the translator to render the original in Tamil into English. We can guess that the translation is meant for non-native speakers especially for those who know English and not Tamil. The translator has to assume that the readers of the translation are not acquainted to the socio-cultural environment of the novel under consideration. The translator’s dictions should be understandable to the readers and at the same time should not mislead them. In spite of the stray instances of lacunae, we must say that the translator has successfully translated Ponniyin Selvan in Tamil into English. The strategies adopted by the translator are highly commendable. A translator can learn many things from the translation strategies adopted by Neelameggham.

Related Papers

Rajendran Sankaravelayuthan

Historical novel is a novel that has as its setting a period of history and that attempts to convey the spirit, manners, and social conditions of a past age with realistic detail and fidelity (which is in some cases only apparent fidelity) to historical fact. The work may deal with actual historical personages, or it may contain a mixture of fictional and historical characters. The historical novel Ponniyin Selvan taken for our analysis is a mixture of fictional and historical characters. The events are also both historical and fictional. Translating such a historical novel is a challenge for the translator. Arguably, the barriers to translation of the historical novel from Tamil to English are even higher since the challenges are many which include taking the readers not only to a new language situation but also to a period in the past. Before resorting to translation, the translator has to be sure that the novel to be translated meets the exacting standards of native English readers of historical fiction. The translator Indra Neelameggham who translated the first part of Ponniyin Selvan has done her job with meticulous care. The translated version can be taken as a model to those who resort to translation of historical novels. The strategies adopted by Indra Neelamggham to make her venture palatable to English readers are highly commendable. So it is worth attempting to learn lessons from her translated work.

Samyukta India Press

Sreedevi K . Nair

A collection of thirty world- class stories which breathe the little- known culture of Kerala, India. These stories of exemplary craftsmanship contest an isolated, insular world view, and celebrate a truly inclusive one not just of human beings but of plants, insects, birds, and animals. T. Padmanabhan’s stories, told in a language of lyrical excellence, transform the ordinary, the mundane and the commonplace into timeless stories of compassion, empathy, love and hope. The book carries a scholarly ‘Introduction’, a highly insightful ‘Translator’s Note’, and a fascinating ‘Interview’ of the author.

sushil kumar (Dr)

The present research paper is an attempt to understand the Translation of Indian Literature into English from historical Perspective. India has long tradition of translating the texts from one to another language. It begins with the translation of Ramayan and Mahabharat from Sanskrit to local Indian languages. Doubtlessly, English is the language of global market and also well accepted in the multinational country like India, therefore Indian literature translated into English has its unique place. Besides, in order to accelerate the interest in translation, many agencies and institution like Sahitya Academy, National Book Trust, and National Translation Mission have been established for this purpose. The translation of India literatures into English language is a vital transformation for providing a space for sharing the Indian literature with not knowing Indian languages readers. By using the tool of translation, Indian rich literary traditions has been relocated and reasserted in world literature scenario. Besides, an attempt is made to comprehend the colonial designs behind the translation of that era and similarly the compulsions (market) of the postcolonial era are to be discussed. The present proposal is an attempt to understand the translation of Indian literature into English during the colonial and postcolonial era particularly in socio-political and historical contexts. Even though colonial and postcolonial eras have their own historical and socio-political specificities, various aspects related to translation of Indian Literature into English can be seen

Md. Nahid Kamal

Throughout the medieval period a lot of retelling has taken place in the literary arena of different Indian languages. This trend was led by Ramayana and Mahabharata, and by retelling we got texts like the Sarala Mahabharata in Odia and Krittivasi Ramayana in Bangla. In this paper, I shed light on another tradition of retelling in Bangla in the medieval period, and this tradition was not derived from traditional Sanskrit and Prakrit, rather it was derived from Perso-Arabic tradition of literature that was prominent at that time. Alaol's Padmavati was written in Bangla and the narrative and illustration was taken from Jaisi's Padmavati, which was written in Awadhi language and originally in the Persian Nasta-liq script. In my writing, I used Syed A. Sayeed's conception on retelling and how a narrative develops through this. I also used Raymond William's conception on structure of feeling to understand prominent discourse in literature at that time, and popular response towards it.

Nandhi Varman

For the project Translations of Sangam Tamil Classics in foreign languages apart from books, e-books of those translations, reports were too submitted. Volume II of the Report is here

Language in India

JS Anantha Krishnan

The world ‘translation’ owes its origin to the old French word ‘tranlacion’. This word was used widely in the 12th century and refers to the transfer of the relics of saints from one place to another. Translation in the current age serves the same purpose. It is an attempt at preserving the rich cultural heritage of mankind in the premise of a different linguistic tradition. Such an attempt but poses serious challenges for the translator. Transcending the obstructions and thereby preserving the original source primarily rises the question whether the writer should prioritize the language skills, or the emotional content delivered. The paper analyses this aspect of translation primarily with reference to Tha Sri Gururaj’s translation of Lakshmipathi Kolara’s Post Box No.9. It also emphasizes on the cultural blending in the play and how this gets confronted by Tha Sri Gururaj in the course of his translation of the play

Herman Tieken

Review article of: Eva Wilden: Naṟṟiṇai. A Critical Edition and an Annotated Translation of the Naṟṟiṇai. Vols I–III. Critical Texts of Cankam Literature – 1.1–3. Chennai 2008; ead. Kuṟuntokai. A Critical Edition and an Annotated Translation of the Kuṟuntokai. Vols I–III. Critical Texts of Cankam Literature – 2.1–3. Chennai 2010. Martha Ann Selby: Tamil Love Poetry. The Five Hundred Short Poems of the Aiṅkuṟunūṟu. New York: Columbia University Press, 2011.

tanutrushna panigrahi

The paper reflects on the translation of Lewis Carroll‟s „Alice in Wonderland‟ into Odia, an Indian vernacular language. It is produced as one of the numerous books under the Biswa Sahitya Granthamala project of a renowned Indian publisher, Granthamandir in the midtwentieth century. The paper aims at analyzing the translation strategy used for translating the text for the non-English speaking readers of a provincial Indian State, Odisha. We intend to examine the relationship between the purpose of translation, receptivity of the target readers and the chosen strategy. It is observed that the translation is neither simplification nor Odianization of the ST. We shall explore both verbal and visual strategies used for translation of Carroll into Odia.

Translation Today

Dr. Vrinda Varma

Aithihyamala (1909) is a compilation of oral legends and folktales in Malayalam by Kottarathil Shankunni. A hundred years since its first publication, and many translations hence, re-translating it into English to suit the contemporary reader comes with its own share of challenges. Overcoming the barrier of archaic language was one thing as was the translation of cultural contexts and culture itself.Butmore demanding was the employment of a contemporary politically correct lens to the stories themselves, and exercising it in translation in such a manner that while the translation and the translatordo remain invisible, the text is suitably modified in places so that blatant prejudices and partisanship inherent in the text do not overshadow the stories themselves. The paper discusses how the translator employed either domestication or foreignization and sometimes a combination of both in order to make sense of the canonical Malayalam text in English, and the rationale for employing ea...

ROSITTA JOSEPH

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THESIS in Tamil

Do you know THESIS in Tamil? How to use THESIS in Tamil and how to say THESIS in Tamil? How to write THESIS in Tamil ? Now let's learn how to say THESIS in Tamil language.

THESIS translate to Tamil meanings: ஆய்வறிக்கை . In other words, ஆய்வறிக்கை in Tamil is THESIS in English. Click to pronunce

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How to use THESIS in Tamil?

How to say thesis in tamil, how to write thesis in tamil, why we should learn tamil language, alphabet in tamil, about tamil language, tamil language code, conclusion on thesis in tamil.

Meaning of THESIS in Tamil language is: ஆய்வறிக்கை .

Other words in Tamil

• ANTHESIS: மலரும்பருவம்.
• ANTIMETATHESIS: எதிர் முரணணி.
• ANTITHESIS: நேர்மாறு.
• BIOSYNTHESIS: உயிரிணைவாக்கம்.
• CHEMOSYNTHESIS: வேதிச்.
• DIATHESIS: டயாஸ்தீசிஸ்.
• ELECTROSYNTHESIS: மின்வழித்.
• EPENTHESIS: ஒரு சொல்லின் இடையில் ஒரு எழுத்தையோ (அ) ஒரு சொல் அசையையோ சேர்த்தல்.
• EPITHESIS: செயற்கைக்கால்.
• HYPOTHESIS: கருதுகோள்.
• What is TROD in Tamil?
• What is TRAMPLE in Tamil?
• What is THURSDAYS in Tamil?
• What is THEODOSIA in Tamil?
• What is TURF in Tamil?

Additional definition and meaning of THESIS in Tamil language

There are many, many reasons why learning a new language is a good idea. It allows you to communicate with new people. It helps you to see things from a different perspective, or get a deeper understanding of another culture. It helps you to become a better listener. It even has health benefits, as studies have shown that people who speak two or more languages have more active minds later in life!

7 reasons to learn a Tamil language

• Makes you smarter.
• Boosts academic achievement.
• Provides professional and career advantages.
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The standard way to write "THESIS" in Tamil is: ஆய்வறிக்கை

See more about Tamil language in here .

Tamil (/ˈtæmɪl/; தமிழ் Tamiḻ [t̪amiɻ], About this soundpronunciation (help·info)) is a Dravidian language natively spoken by the Tamil people of South Asia. Tamil is the official language of the Indian state of Tamil Nadu, and an official language of the two sovereign nations, Singapore and Sri Lanka. In India, it is also the official language of the Union Territory of Puducherry. Tamil is spoken by significant minorities in the four other South Indian states of Kerala, Karnataka, Andhra Pradesh and Telangana and the Union Territory of the Andaman and Nicobar Islands. It is also spoken by the Tamil diaspora found in many countries, including Malaysia, South Africa, United Kingdom, United States, Canada, Australia and Mauritius. Tamil is also natively spoken by Sri Lankan Moors. One of 22 scheduled languages in the Constitution of India, Tamil was the first to be classified as a classical language of India and is one of the longest-surviving classical languages in the world. A. K. Ramanujan described it as "the only language of contemporary India which is recognizably continuous with a classical past." The variety and quality of classical Tamil literature has led to it being described as "one of the great classical traditions and literatures of the world"..

Writing system in Tamil

Tamil (Brahmic), Tamil-Brahmi (historical), Grantha (historical), Vatteluttu (historical), Pallava (historical), Kolezhuthu (historical), Arwi (Abjad), Tamil Braille (Bharati), Latin script (informal)

Tamil Speaking Countries and Territories

Tamil Speaking Countries and Territories: India, Sri Lanka.

Tamil native speakers

Tamil native speakers: 75 million (2011–2019), L2 speakers: 8 million (2011).

Tamil language code is: ta.

Now that you have learned and understood the common ways of saying THESIS in Tamil is "ஆய்வறிக்கை", it's time to learn how to say THESIS in Tamil. This will hopefully give you a little motivation to study Tamil today.

ஆய்வறிக்கை in Tamil meanings THESIS in English .

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