Hidden Markov Model Based Part of Speech Tagger for Sinhala Language

TL;DR

This paper introduces a Hidden Markov Model based POS tagger for Sinhala, a morphologically rich language, achieving over 90% accuracy on known words through a statistical approach.

Contribution

It presents the first HMM-based POS tagging system specifically designed for Sinhala, addressing its morphological complexity.

Findings

Achieved over 90% accuracy on known words

Utilized a corpus-based statistical approach

Demonstrated effectiveness for Sinhala language processing

Abstract

In this paper we present a fundamental lexical semantics of Sinhala language and a Hidden Markov Model (HMM) based Part of Speech (POS) Tagger for Sinhala language. In any Natural Language processing task, Part of Speech is a very vital topic, which involves analysing of the construction, behaviour and the dynamics of the language, which the knowledge could utilized in computational linguistics analysis and automation applications. Though Sinhala is a morphologically rich and agglutinative language, in which words are inflected with various grammatical features, tagging is very essential for further analysis of the language. Our research is based on statistical based approach, in which the tagging process is done by computing the tag sequence probability and the word-likelihood probability from the given corpus, where the linguistic knowledge is automatically extracted from the annotated corpus. The current tagger could reach more than 90% of accuracy for known words.