A Machine-Learning Approach to Estimating the Referential Properties of Japanese Noun Phrases

TL;DR

This paper presents a machine-learning method to automatically estimate the referential properties of Japanese noun phrases, improving efficiency over manual rule-based approaches for translation and anaphora resolution.

Contribution

It introduces an automated score adjustment technique for referential property classification, reducing manual effort in Japanese NLP tasks.

Findings

Successfully reduced manpower for score adjustment

Achieved effective classification of Japanese noun phrases

Demonstrated applicability to machine translation and anaphora resolution

Abstract

The referential properties of noun phrases in the Japanese language, which has no articles, are useful for article generation in Japanese-English machine translation and for anaphora resolution in Japanese noun phrases. They are generally classified as generic noun phrases, definite noun phrases, and indefinite noun phrases. In the previous work, referential properties were estimated 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. In this work, we automatically adjusted these scores by using a machine-learning method and succeeded in reducing the amount of manpower needed to adjust these scores.