Abstract
The referential properties of noun phrases are useful for article generation in Japanese-English machine translation and in anaphora resolution in Japanese same noun phrases and are generally classified into generic noun phrases, definite noun phrases and indefinite noun phrases. In the previous work, an estimation of referential properties was done by developing rules that used clue words. If two or more rules were in conflict with each other, the category having the maximum total score given by the rules was selected as the desired category. The score given by each rule was established by hand, so the manpower cost was high. This paper describes a machine learning method that reduces the amount of manpower needed to adjust these scores.
