Identification of POS Tag for Khasi Language based on Hidden Markov Model POS Tagger

Authors

  • Sunita Warjri North-Eastern Hill University, Shillong, Meghalaya
  • Partha Pakray National Institute of Technology, Silchar, Assam
  • Saralin Lyngdoh North-Eastern Hill University, Shillong, Meghalaya
  • Arnab Kumar Maji North-Eastern Hill University, Shillong, Meghalaya

DOI:

https://doi.org/10.13053/cys-23-3-3248

Keywords:

Natural language processing (NLP), computational linguistic, part of speech (POS), POS tagger, Hidden Markov Model (HMM)

Abstract

Computational Linguistic (CL) becomes an essential and important amenity in the present scenarios, as many different technologies are involved in making machines to understand human languages. Khasi is the language which is spoken in Meghalaya, India. Many Indian languages have been researched in different fields of Natural Language Processing (NLP), whereas Khasi lacks substantial research from the NLP perspectives. Therefore, in this paper, taking POS tagging as one of the key aspects of NLP, we present POS tagger based on Hidden Markov Model (HMM) for Khasi language. In this present preliminary stage of building NLP system for Khasi, with the analyses of the categories and structures of the words is started. Therefore, we have designed specific POS tagsets to categories Khasi words and vocabularies. Then, the POS system based on HMM is trained by using Khasi words which have been tagged manually using the designed tagsets. As ambiguity is one of the main challenges in POS tagging in Khasi, we anticipated difficulties in tagging. However, by running with the first few sets of data in the experimental data by using the HMM tagger we found out that the result yielded by this model is 76.70% of accurate.

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Published

2019-09-25

Issue

Vol. 23 No. 3 (2019): Thematic issue: Computational Linguistics (Guest Editor A. Gelbukh)

Section

Articles of the Thematic Issue

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