Abstract:
In natural language processing (NLP), Word
segmentation and Part-of-Speech (POS) tagging are
fundamental tasks. The POS information is also
necessary in NLP- based applications such as
machine translation (MT), information retrieval (IR),
etc. Currently, there are many research efforts in
word segmentation and POS tagging developed
separately with various approaches to reach high
performance and accuracy. For Myanmar
Language, there are also separate word segmentors
and POS taggers based on statistical approaches
such as Neural Network (NN) and Hidden Markov
Models (HMMs). However, the Myanmar language
has the complex morphological structure and the
Out-of-Vocabulary (OOV) problem still exists. Thus,
this paper proposed morphological analysis based
joint Myanmar word segmentation and POS tagging
using Hidden Markov Models (HMM) and
morphological rules. This paper has also presented
the comparison of accuracy result
using HMM only, and HMM with morphological
analysis.
