Myanmar Word Stemming and Part-of-Speech Tagging using Rule Based Approach

Abstract

Myanmar language is spoken by more than 33 million people and use itas an official language of the Republic of the Union of Myanmar in bothverbal and written communication. With the rapid growth of digital content in Myanmar Language, applications like machine learning, translation and information retrieval become popular and it required to obtain the effective Natural Language Processing (NLP) studies.The main objective of this paper is to study Myanmar words morphology, to implement n-gram based word segmentation and to propose grammatical stemming rules and POS tagging rules for Myanmar language. So, this paper proposed the word segmentation, stemming and POS tagging based on n-gram method and rule-based stemming method that has the ability to cope the challenges of Myanmar NLP tasks. The proposed system not only generates the segmented words but also generates the stemmed words with POS tag by removing prefixes, infixes and suffixes. The proposed system provides 80% to 85 % accuracy. The data are collected from several online sources and the system is implemented using Python language.