Structural Parsing of Natural Language Text in Tamil Language Using Dependency Model

摘要

Parsing is an important process of Natural Language Processing (NLP) and Computational Linguistics which is used to understand the syntax and semantics of natural language sentences confined to the grammar. Parsing models need syntax and semantic coverage for better interpretation of natural language sentences. Though statistical parsing with trigram language models gives better performance through tri-gram probabilities and large vocabulary size, it has some disadvantages like lack of support in syntax, free ordering of words and long distance relationship which are the challenging features of the Tamil language. Grammar based structural parsing provides solutions to some extent. To overcome these disadvantages, structural component is to be involved in statistical approach which results in hybrid models like phrase and dependency models. To add the structural component, balance the vocabulary size and meet the challenging features, lexicalized and statistical parsing (LSP) is to be employed with the assistance of hybrid models. To incorporate all the features in complex and large sentences, phrase structure model may not be suitable to a larger extent. When dependency relations are applied among words, direct relationships can be established. Lexicalized and statistical parsing of natural language text in Tamil language using dependency model will give better performance than using phrase structure model. New part of speech (POS) and dependency tag sets for Tamil language have beenrnTreebank has been developed with 326 sentences which comprises more than 5000 words with manual annotation. It has been extended to 1000 sentences using bootstrapping and manual correction and used to train the dependency model. This LSP with dependency model provides better results and covers all the features of Tamil language.