Tamil Morphological Analysis

An Efficient Rule-Based System for Morphological Parsing of Tamil Language

தமிழஉருபனியலஆயவு

STUDENTS:Karthik S 106106029Praveen Kumar 106106045Venkataraman GB 106106073

GUIDE:Dr. V. Gopalakrishnan

Final Semester ProjectDepartment of Computer Science and EngineeringNational Institute of Technology, Tiruchirappalli

May 2010

Agenda Overview of the Project NLP Applications – The Stakeholders The problem at hand The proposed solution

◦ Rule – Based Morphological Analysis◦ Machine Learning

Where does it all fit in ? Need for Tamil Morphological Analysis Resources Obtained Implementation Details Demonstration Future Scope

08/04/2023National Institute of Technology,

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WHO WHAT WHYWHERE HOW1

Overview of the Project Natural Language Processing Morphological Analysis Tamil Language


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WHO WHAT WHYWHERE HOW

Morphing …

… And in Tamilநடநதான நடநதனர

நடககினறாள

நடபபான

நடககினறான

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NLP Appl icat ions – The Stakeholders

WHO ARE THE STAKEHOLDERS ?Natural Language Processing Applications like: Stemming Machine Translation Speech Recognition Information Retrieval


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WHY ARE THESE APPLICATION THE STAKEHOLDERS ?

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The problem at handMorphological Analysis of Tamil involves understanding the word structure and its inflections

AGGLUTINATION IN TAMIL Agglutination is the morphological process of adding affixes to the base of a word Typical Tamil verb form will have a number of suffixes showing person, number,

mood, tense and voice.

INFLECTIONS IN TAMIL


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பால - Gender

எண - Number

திணை - Class

காலம - Tense இடம - Person

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The problem at handMorphological Analysis of Tamil involves understanding the word structure and its inflections

AGGLUTINATION IN TAMIL Agglutination is the morphological process of adding affixes to the base of a word Typical Tamil verb form will have a number of suffixes showing person, number,

mood, tense and voice.

INFLECTIONS IN TAMIL Example: vAlntukkontiruntēṉ: [வாழநதுககாணடிருநதேதன]


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vAl - வாழ intu - நது kontu - ககாணடு irunta - இருநத ēn - ஏன

root voice marker tense marker aspect marker person marker

live past tenseobject voice

during past progressive first person,Singular

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The proposed solution There are two levels called lexical and surface levels. In the surface level, a word is represented in its original orthographic form. In the lexical level, a word is represented by denoting all of the functional components of the word.

RULE – BASED MORPHOLOGICAL ANALYSISAnalyzing word inflections using rules specified in Tamil Grammar

அன ஆன அள ஆள அர ஆர பமமார

அஆ குடுதுறு என ஏன அல அன

அம ஆம எம ஏம ஓமமா டுமமூர

கடதற ஐ ஆய இமமின இரஈர

ஈயர கயவு மமனபவும பிறவும

விணை&யின விகுதி மபயரினும சிலவேவ


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SURFACE LEVEL LEXICAL LEVEL

நனனூல

மதாலகாபபியம

The proposed solution MACHINE LEARNING APPROACH


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While checking for suffixes in a given word, more than one suffix might be possible, if the rules are strictly followed. But only one suffix is semantically possible.

விகுதி : படிதது – “ ” உ படிததது – “ ” து or “ ” உ ???

M/L approach helps the system in “learning” the correct parsing method for the word, and in the subsequent processing of the same word, the wrong possibilities are automatically eliminated.

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Two words might share the same inflectional part.

நடககினறான படிககினறான

The inflectional part of every word is learnt by the system. This helps in optimization by eliminating the need to analyse the second word again from scratch

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Where does it all fit in ?


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Characters

Word – Tokenization

Morphological Analysis

Sentence Syntax Analysis

Semantic Analysis

ப டி த தா ன

படிததான

படி - தத - ஆன

அவன புததகதணைதப படிததான

Meaning of the sentence ???

Need for Tamil Morphological AnalysisENGLISH vs. TAMIL

TRANSLATION AND SEMANTIC ANALYSIS

அவன மதுரை$ககு வநதாள -- Semantically Wrong

To check semantic correctness of a sentence, morphological analysis is needed. How to translate the above sentence ??


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I came நான வநதேதனYou came ந வநதாயThey came அவரகள வநதனர

He came அவன வநதானShe came அவள வநதாள

Resources ObtainedEMILLE – CIIL TAMIL MONOLINGUAL CORPUS Enabling Minority Language Engineering Collaborative Venture of

◦ Lancaster University, UK ◦ Central Institute of Indian Languages (CIIL), Mysore, India

Distributed by European Language Resources Association [ELRA]

TAMIL WORDNET The database is a semantic dictionary that is designed as a lexical network Developed by

◦ Department of Linguistics of Tamil University◦ AU-KBC Research Centre, Chennai

Tamil Wordnet resembles a traditional dictionary. It also contains valuable information about morphologically related words


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Implementation Details - 1


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Input Tamil Word

Check in DB

C-V Segmentation

Root verb

?

Backward Scanning of inflections

Classify and Remove Inflection

Output

Conflict ResolutionMachine Learning

No

YesYes

No

Implementation Details - 2


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படிததான

ப டி த தா ன

ப - அ ட - இ த த - ஆ ன

ப அ ட இ த த ஆ ன

படி < VERB_ROOT >

தத < PAST TENSE >

ஆன < 3SM >

Implementation Details - 3UNICODE SUPPORT FOR TAMIL U+0B80 – U+0BFF

GOOGLE TAMIL TRANSLITERATOR IME (Input Method) Google Transliteration IME is an input method editor which allows users to

enter text Tamil using a roman keyboard

PROGRAMMING LANGUAGE Java

DATABASES MySQL Databases, with JDBC to access the database


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Implementation Details - 3TRANSLITERATION MODULE A simple Transliterator module - to enable conversion from Tamil to English

and vice-versa Example:

◦ அ - a◦ ஆ - aa◦ க - ka

HASH TABLE GENERATOR The application uses two data files, containing a list of vigudhi and idainilai. The Java Hash Generator Code loads the data from the workbooks, adds

them to a hash table, and serializes the data and outputs to an external data file, which can be loaded whenever the application requires access.


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Future Scope The algorithm can be extended to cover nouns and noun forms too.

The algorithm can be improved to incorporate stricter rules so as to reduce conflicts that arise in the output generated by the current system.

The algorithm can be extended for other agglutinative languages.

The various resources obtained as a part of this project, including the EMILLE-CIIL ELRA Corpus, the Tamil Wordnet Database and other tools can be used for further study, research and development in the field of Natural Language Processing at our college in the years to come.


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References A Novel Approach to Morphological Analysis for Tamil Language

◦ Anand kumar M1, Dhanalakshmi V1, Rajendran S2, Soman K P Nannool and Tholkaapiyam

◦ Tamil Grammar texts The Morphological Generator and Parsing Engine for Tamil Verb

Forms. ◦ Ultimate Software Solution, Dindigul

Morphological Analyzer for Tamil ◦ Anandan. P, Ranjani Parthasarathy, Geetha T.V. [2002]◦ ICON 2002, RCILTS-Tamil, Anna University, India.

Morphology. A Handbook on Inflection and Word Formation◦ Daelemans Walter, G. Booij, Ch. Lehmann, and J. Mugdan (eds.) [2004]

Tamil Part-of-Speech tagger based on SVMTool◦ Dhanalakshmi V, Anandkumar M, Vijaya M.S, Loganathan R, Soman K.P, Rajendran S [2008]◦ Proceedings of the COLIPS International Conference on Asian Language Processing 2008 (IALP).

Unsupervised Learning of the Morphology of a Natural Language.◦ John Goldsmith. [2001]◦ Computational Linguistics, 27(2):153–198.

Computational morphology of verbal complex ◦ Rajendran, S., Arulmozi, S., Ramesh Kumar, Viswanathan, S. [2001]◦ Paper read in Conference at Dravidan University, Kuppam, December 26-29, 2001.


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Thank you


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Documents

Tamil Morphological Analysis