V. Dhanalakshmi

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.

Morphological Analyzer for Telugu Using Support Vector Machine

In this paper, we presented a morphological analyzer for the classical Dravidian language Telugu using machine learning approach. Morphological analyzer is a computer program that analyses the words belonging to Natural Languages and produces its grammatical structure as output. Telugu language is highly inflection and suffixation oriented, therefore developing the morphological analyzer for Telugu is a significant task. The developed morphological analyzer is based on sequence labeling and training by kernel methods, it captures the non-linear relationships and various morphological features of Telugu language in a better and simpler way. This approach is more efficient than other morphological analyzers which were based on rules. In rule based approach every rule is depends on the previous rule. So if one rule fails, it will affect the entire rule that follows. Regarding the accuracy our system significantly achieves a very competitive accuracy of 94% and 97% in case of Telugu Verbs and nouns. Morphological analyzer for Tamil and Malayalam was also developed by using this approach.

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.