Various efforts have been made in developing machine translation (MT) systems for practical use.
Historically, there are many approaches on MT research: transfer-based, interlingua-based, and etc.
Among these approaches, the most distinctive are rule-based and corpus-based methods. Research on the
corpus-based approach has emphasized on the importance of text corpora used as a source for linguistic
and knowledge databases. There have been two major approaches among the corpus-based MT known as
statistics-based and example-based. It might be said that all approaches have their own pros and cons.
Therefore some MT researchers have selected and combined them together for creating a new effective
model. We also combine two potential approaches to produce our own strategy; namely, rule-based and