dc.contributor.author |
Myint, Phyu Hninn
|
|
dc.date.accessioned |
2019-07-25T04:26:16Z |
|
dc.date.available |
2019-07-25T04:26:16Z |
|
dc.date.issued |
2010-12-16 |
|
dc.identifier.uri |
http://onlineresource.ucsy.edu.mm/handle/123456789/1262 |
|
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 |