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Assigning automatically Part-of-Speech tags to build tagged corpus for Myanmar language

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dc.contributor.author Myint, Phyu Hninn
dc.date.accessioned 2019-07-22T06:34:33Z
dc.date.available 2019-07-22T06:34:33Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1133
dc.description.abstract A variety of Natural Language Processing (NLP) tasks, such as named entity recognition, stemming, question answering and machine translation, benefit from knowledge of the words syntactic categories or Part-of-Speech (POS). POS taggers must be successfully applied to assign a single best POS to every word in a corpus.This paper presents to develop Part-of-Speech tagged text corpora by employing Bigram part-of-speech tagger. POS tagging is a process of assigning appropriate syntactic categories to each word in a sentence. As applying bigram model for automated tagging process we have provided an adequate annotated corpus from scratch. We have used customized POS tagset to annotate the words in a Myanmar sentence. Our Bigram tagger has two phases: training with Hidden Markov Models (HMM) and decoding with Viterbi algorithm. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject Natural Language Processing en_US
dc.subject Part-of- Speech Tagging en_US
dc.subject Hidden Markov Models and Viterbi en_US
dc.title Assigning automatically Part-of-Speech tags to build tagged corpus for Myanmar language en_US
dc.type Article en_US


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