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Investigating Part-of-Speech Tagging in Khasi Using Naïve Bayes and Support Vector Machine

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Sunita Warjri
Partha Pakray
Saralin A. Lyngdoh
Arnab Kumar Maji
Published in
International Conference on Innovative Computing and Communications
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the initial efforts of applying Naïve Bayes and Support Vector Machine models for part-of-speech tagging in the Khasi language, spoken by the indigenous people of Meghalaya, India. The research involves the creation of a manually annotated Khasi PoS corpus and the evaluation of these models on this corpus, yielding impressive accuracy rates. The study highlights the challenges of ambiguities and orthographic issues in the Khasi language and compares the results with state-of-the-art approaches. The chapter also discusses the potential for future work in expanding the corpus and addressing the linguistic challenges specific to Khasi. The experimental results and comparisons with other Indian languages underscore the significance of this work in advancing the computational linguistics of under-resourced languages.
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Title
Investigating Part-of-Speech Tagging in Khasi Using Naïve Bayes and Support Vector Machine
Authors
Sunita Warjri
Partha Pakray
Saralin A. Lyngdoh
Arnab Kumar Maji
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
International Conference on Innovative Computing and Communications

Print ISBN: 978-981-19-3678-4

Electronic ISBN: 978-981-19-3679-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-3679-1_11
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