S Rajendran

Morphological Analyzer for Agglutinative Languages Using Machine Learning Approaches

This paper is based on morphological analyzer using machine learning approach for complex agglutinative natural languages. Morphological analysis is concerned with retrieving the structure, the syntactic and morphological properties or the meaning of a morphologically complex word. The morphology structure of agglutinative language is unique and capturing its complexity in a machine analyzable and generatable format is a challenging job. Generally rule based approaches are used for building morphological analyzer system. In rule based approaches what works in the forward direction may not work in the backward direction. This new and state of the art machine learning approach based on sequence labeling and training by kernel methods captures the non-linear relationships in the different aspect of morphological features of natural languages in a better and simpler way. The overall accuracy obtained for the morphologically rich agglutinative language (Tamil) was really encouraging.

Chunker for Tamil

This paper presents the chunker for Tamil using Machine learning techniques. Chunking is the task of identifying and segmenting the text into syntactically correlated word groups. The chunking is done by the machine learning techniques, where the linguistical knowledge is automatically extracted from the annotated corpus. We have developed our own tagset for annotating the corpus, which is used for training and testing the POS tagger generator and the chunker. The present tagset consists of thirty tags for POS and nine tags for chunking. A corpus size of two hundred and twenty five thousand words was used for training and testing the accuracy of the Chunker. We found that CRF++ affords the most encouraging result for Tamil chunker.

Morphological analyzer for malayalam using machine learning

An efficient and reliable method for implementing Morphological Analyzer for Malayalam using Machine Learning approach has been presented here. A Morphological Analyzer segments words into morphemes and analyze word formation. Morphemes are smallest meaning bearing units in a language. Morphological Analysis is one of the techniques used in formal reading and writing. Rule based approaches are generally used for building Morphological Analyzer. The disadvantage of using rule based approaches are that if one rule fails it will affect the entire rule that follows, that is each rule works on the output of previous rule. The significance of using machine learning approach arises from the fact that rules are learned automatically from data, uses learning and classification algorithms to learn models and make predictions. The result shows that the system is very effective and after learning it predicts correct grammatical features even forwords which are not in the training set.

Grammar Teaching Tools for Tamil language

Grammar plays an important role in good communication. Learning grammar rules for Tamil language is very difficult as they have a very rich morphological structure which is agglutinative. Students get annoyed with the language rules and the old teaching methodology. Computer assisted Grammar Teaching Tools makes students to learn faster and better. NLP applications are used to generate such tools for curriculum enhancement of the students. In this paper we present the Grammar teaching tools in the sentence and word analyzing level for Tamil Language. The tools like Parts of speech Tagger, Chunker and Dependency parser for the sentence level analysis and Morphological Analyzer and Generator for the word level analysis were developed using machine learning based technology. These tools were very useful for second language learners to understand the word and sentence construction in a non-conceptual way. An user interface is developed for the practical usage of the tool.

A novel approach to morphological generator for tamil

Tamil is a morphologically rich language. Being agglutinative language most of the categories expressed are suffixes. Tamil is a post positional inflectional language. The Morphological Generator takes lemma and a Morpho-lexical description as input and gives a word-form as output. It is a reverse process of Morphological Analyzer. Morphological generator system implemented here is a new data driven approach which is simple, efficient and it does not require any rules and morpheme dictionary. We have developed an individual system to handle nouns and verbs. Any automated machine translation system requires morphological analyzer of source language and morphological generator of the target language. Using this morphological generator we have also developed a verb conjugator and noun declension. Here.