Abstract:
This paper proposes an approach to
annotate function tags for unparsed text. We address
the question of whether data-driven function tag
assignment method can be applied to Myanmar
language. We assign function tags directly basing on
lexical information, which is easily scalable for
languages that lack sufficient parsing resources or
have inherent linguistic challenges for parsing. We
investigate a supervised sequence learning method to
automatically recognize function tags. In order to
demonstrate the effectiveness and versatility of our
method, we investigate function tag assignment for
unparsed text by applying Naïve Bayesian theory.
Our approach to functional analysis is to classify, so
far as possible, all the processes and states which
languages must describe, and to identify the
functional elements which are needed for each one to
construct a meaningful sentence.
