Morpheme based Language Model for Tamil Part-of-Speech Tagging

Authors: S. Lakshmana Pandian, T.V. Geetha

Polibits, 38, pp. 19-26, 2008.

Abstract: The paper describes a Tamil Part of Speech (POS) tagging using a corpus-based approach by formulating a Language Model using morpheme components of words. Rule based tagging, Markov model taggers, Hidden Markov Model taggers and transformation-based learning tagger are some of the methods available for part of speech tagging. In this paper, we present a language model based on the information of the stem type, last morpheme, and previous to the last morpheme part of the word for categorizing its part of speech. For estimating the contribution factors of the model, we follow generalized iterative scaling technique. Presented model has the overall F-measure of 96%.

Keywords: Bayesian learning; language model; morpheme components; generalized iterative scaling

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