Author:
Cing Dim Lam,Soe Khin Mar
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’s preprocessing work 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 different methods to get 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). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.
Publisher
Institute of Advanced Engineering and Science
Subject
Electrical and Electronic Engineering,General Computer Science
Cited by
2 articles.
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1. Part-of-Speech Tagging Accuracy for Manufacturing Process Documents and Knowledge;Lecture Notes in Networks and Systems;2024
2. Pos Taging of Uzbek Text Using Hidden Markov Model;2023 8th International Conference on Computer Science and Engineering (UBMK);2023-09-13