Sachi Vinayak, Angle and Rao, Ashwath B and Murali Krishna, S N (2018) Kannada Morpheme Segmentation using Machine Learning. International Journal of Engineering and Technology, 07 (31). pp. 45-49. ISSN 0975-4024

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Abstract

This paper addresses and targets morpheme segmentation of Kannada words using supervised classification. We have used manually annotated Kannada treebank corpus, which is recently developed by us. Kannada bears resemblance to other Dravidian languages in morphological structure. It is an agglutinative language, hence its words have complex morphological form with each word comprising of a root and an optional set of suffixes. These suffixes carry additional meaning, apart from the root word in a context. This paper discusses the extraction of morphemes of a word by using Support Vector Machines for Classification. Additional features representing the properties of the Kannada words were extracted and the different letters were classified into labels that result in the morphological segmentation of the word. Various methods for evaluation were considered and an accuracy of 85.97% was achieved.

Item Type:	Article
Uncontrolled Keywords:	Morphology, Kannada, Machine Learning
Subjects:	Engineering > MIT Manipal > Computer Science and Engineering
Depositing User:	MIT Library
Date Deposited:	03 Jul 2018 10:00
Last Modified:	03 Jul 2018 10:00
URI:	http://eprints.manipal.edu/id/eprint/151463

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