Abstract:
Word embedding represents the words in terms of
vectors. It is influenced on different NLP research
areas such as document classification, author
identification, sentiment analysis, etc. One of the
most popular embedding models is Word2Vec model.
It provides efficient representations of words by using
Continuous Bag of Words model (CBOW) and Skip
Gram model. In English language, word embedding
model can be applied for data preprocessing well but
there is a very little amount of work done in
Myanmar language. Text preprocessing is important
part to build embedding model and it is a
significantly effect on final results. This paper tries to
extract the analogous words between Myanmar news
articles focus on the bag of words (CBOW) model
using different features vector sizes. By analyzing
word embedding model are obtained the better
results with a high dimensional vectors than a low
dimensional vectors to cluster the words based on its
relatedness.
