In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.

In this paper, we propose a learning classifier based on maximum entropy (ME) for resolving zero-anaphora in Chinese text. Besides regular grammatical, lexical, positional and semantic features motivated by previous research on anaphora resolution, we develop two innovative Web-based features for extracting additional semantic information from the Web. The values of the two features can be obtained easily by querying the Web using some patterns. Our study shows that our machine learning approach is able to achieve an accuracy comparable to that of state-of-the-art systems. The Web as a knowledge source can be incorporated effectively into the ME learning framework and significantly improves the performance of our approach.

Zero-pronouns and overt pronouns occur frequently in Japanese text. These must be interpreted by recognizing their antecedents to properly understand' a piece of discourse. The notion of centering" has been used to help in the interpretation process for intersentential anaphors. This is based on the premise that in a piece of discourse, some members have a greater amount of attention put on it than other members. In Japanese, the zero-pronoun is said to have the greatest amount of attention put on it. But, when there are more than one zero-pronoun in a sentence, only one of them would be accountable using centering. Overt pronouns and any other zero-pronouns may as well have appeared as ordinary' noun phrases. In this paper, the notion of centering has been extended so that these can also be interpreted. Basically, zero-pronouns and overt pronouns are treated as being more centered" in the discourse than other ordinary' noun phrases. They are put in an ordered list called the Center List. Any other noun phrases appearing in a sentence are put in another list called the Possible Center List. Noun phrases within both lists are ordered according to their degrees of salience. To see the effect of our approach, it was implemented in a simple system with minimal constraints and evaluated. The result showed that when the antecedent is in either the Center List or the Possible Center List, 80% of all zero-pronouns and overt pronouns were properly interpreted.