Morphological analyzer and generator for Tamil Language

Lushanthan, S.; Weerasinghe, A. R.; Herath, Dulip Lakmal

doi:10.1109/icter.2014.7083900

Cited by 15 publications

(2 citation statements)

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“…It analyzed various types of Bangla locative case sentences in favor of UNL structure considering the lexicon and UNL relations they create. S. Lushanthan et al [6] developed Morphological Analyzer and Generator for Tamil Language. It illustrates how the lexicon and the orthographic rules of Tamil language had been written as regular expressions using only finite state operations and how this approach had been implemented to develop a morphological generator/analyzer.…”

Section: Literarure Surveymentioning

confidence: 99%

UNL Enconversion for Tamil Sentence

Laxmi¹

2019

IJRASET

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Natural language processing (NLP) aims to design and build software that will analyze, understand and generate languages that humans use naturally. Machine translation is a very important application in natural language processing. Universal Networking Language (UNL) is an intermediator which exchanges the knowledge from a natural language to allow the access of its content through different languages. In this proposed work, the development of an enconverter (analyzer) process is done in order to overcome language barriers from Tamil sentence to UNL format. The grammar teaching tools like character analyzer for analyzing character, morphological analyzer and verb conjugator for the word level analysis, Parts of speech (POS) tagger, chunker and dependency parser for the sentence level analysis will be developed using machine learning based technology. The purpose is to enconvert the Tamil sentence to UNL format so that it can be easily translated to any other targeted natural languages.

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Section: Literarure Surveymentioning

confidence: 99%

UNL Enconversion for Tamil Sentence

Laxmi¹

2019

IJRASET

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“…Morphological analyzers are used in developing NLP applications such as machine translation, spell checkers, speech recognition, grammar checker, information retrieval, Part of speech tagging, automatic sentence construction, etc. Despite their importance, some languages, such as Tigrigna, need more publicly available morphological analyzers [8]. Therefore, this research paper focuses on developing Tigrigna verbs morphological analyzer.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

IJNLC

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Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.

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