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http://dx.doi.org/10.6109/jicce.2020.18.4.207

A Semi-supervised Learning of HMM to Build a POS Tagger for a Low Resourced Language  

Pattnaik, Sagarika (Department of Computer Science and Engineering, SOA, Deemed to be University)
Nayak, Ajit Kumar (Department of Computer Science and Information Technology, SOA, Deemed to be University)
Patnaik, Srikanta (Department of Computer Science and Engineering, SOA, Deemed to be University)
Publication Information
Journal of information and communication convergence engineering / v.18, no.4, 2020 , pp. 207-215 More about this Journal
Abstract
Part of speech (POS) tagging is an indispensable part of major NLP models. Its progress can be perceived on number of languages around the globe especially with respect to European languages. But considering Indian Languages, it has not got a major breakthrough due lack of supporting tools and resources. Particularly for Odia language it has not marked its dominancy yet. With a motive to make the language Odia fit into different NLP operations, this paper makes an attempt to develop a POS tagger for the said language on a HMM (Hidden Markov Model) platform. The tagger judiciously considers bigram HMM with dynamic Viterbi algorithm to give an output annotated text with maximum accuracy. The model is experimented on a corpus belonging to tourism domain accounting to a size of approximately 0.2 million tokens. With the proportion of training and testing as 3:1, the proposed model exhibits satisfactory result irrespective of limited training size.
Keywords
HMM; NLP; Odia; POS tagger;
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