Comparison of Machine Learning Methods for Tamil Morphological Analyzer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AFSDC288U" target="_blank" >RIV/00216208:11320/22:FSDC288U - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-981-16-2422-3_31" target="_blank" >https://doi.org/10.1007/978-981-16-2422-3_31</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-16-2422-3_31" target="_blank" >10.1007/978-981-16-2422-3_31</a>

Result language

angličtina

Original language name

Comparison of Machine Learning Methods for Tamil Morphological Analyzer

Original language description

Morphological Analysis is the study of word formation which explains how a word is evolved from smaller pieces called root word. Morphological analysis is an important task in natural language processing applications, namely, POS Tagging, Named Entity Recognition, Sentiment Analysis, and Information Extraction. The heart of the morphological analysis process is to find out the root words from the given documents that is exactly matched with the corpus list. There are many research works that have been done in this area of research however not much contribution has been made in domain specific in the area of domain specific analysis regional languages. Morphological analysis for regional languages is complex and demands extensive analysis of natural language rules and syntax pertaining to specific regional language of focus. In order to improve the quality of natural language processing, generally research works are restricted to domain specific analysis. Morphological analysis in Tamil language documents is quite complex and valuable for Tamil NLP process. Our work focuses on a comparative study of three different approaches in performing morphological analysis on the regional language called Tamil. The scope our work is restricted to Gynecology domain text in represented in Tamil language. The analysis of morphological process is done in three different machine learning methods for the Gynecological documents. The performance analysis is carried out on the three morphological analysis models, namely, Rules-based lemmatizer (IndicNLP), Paradigm-based Tamil Morphological Analyzer (Tacola), and N-gram-based lemmatizer (UDPipe), and our experimental results proved that paradigm-based finite state model gives optimal results (0.96).

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Intelligent Sustainable Systems

ISBN

978-981-16-2422-3

ISSN

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e-ISSN

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Number of pages

15

Pages from-to

385-399

Publisher name

Springer

Place of publication

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Event location

Singapore

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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