Morphological Analyzer for Agglutinative Languages Using Machine Learning Approaches

• Main Authors: M., Anand Kumar, C., Arun Kumar, Dhanalakshmi, V., Rekha, R.U., Soman, K.P., Rajendran, S.
• Format: Conference Proceeding
• Language: English
• Subjects: Communications technology; Computer networks; Labeling; Machine learning; Morhological analyzer; Morpheme; Morphology; Natural languages; Paper technology; Search engines; Segmentation; Sequence labeling; Speech analysis; Speech synthesis
• DOI: 10.1109/ARTCom.2009.184

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.