Abstract:
POS tagging is the process of automatic
assigning a for each word with their categories
that best suits the definition of the word as well as
the context of the sentence of a natural language
in which it is used. It is also a fundamental stage
in most natural language processing (NLP) tasks.
There are different approaches to the problem of
POS tagging. This paper describes the usage of
BPNN for Myanmar POS tagging. Experiments
show that our analysis achieves a good result with
simple sentences and complex sentences.
Experimental results are showed that on input
data representation by 3-gram, 4-gram and 5-
gram format. Open and close data are used to
evaluate performance. 3-gram format for close
data and 4-gram format for open data are more
suitable and good results for POS tagging.
