A Novel Approach to Morphological Analysis for Tamil Language

This paper presents the morphological analysis for complex agglutinative Tamil language using machine learning approach. Morphological analysis is concerned with retrieving the structure, syntactic rules, morphological properties and the meaning of a morphologically complex word. The morphological structure of an agglutinative language is unique and capturing its complexity in a machine analyzable and generatable format is a challenging job. Generally rule based approach is used in building morphological analyzer. In rule based approach what works in the forward direction may not work in the backward direction. The Novel approach to morphological analyzer is based on sequence labeling and training by kernel methods. It captures the non-linear relationships and various morphological features of Tamil language in a better and simpler way. The efficiency of our system is compared with the existing morphological analyzers which are available in net. Regarding the accuracy our system significantly outperforms the existing morphological analyzer and achieves a very competitive accuracy of 95.65% for Tamil language.