Zero anaphora resolution in chinese and its application in chinese-english machine translation

Authors:
Jing Peng;Kenji Araki
Affiliations:
Language Media Laboratory, Hokkaido University, Sapporo, Japan;Language Media Laboratory, Hokkaido University, Sapporo, Japan
Venue:
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Year:
2007

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Abstract

In this paper, we propose a learning classifier based on maximum entropy (ME) for resolving ZA in Chinese. Besides regular grammatical, lexical, positional and semantic features, we develop two innovative Web-based features for extracting additional semantic information of ZA from the Web. Our study shows the Web as a knowledge source can be incorporated effectively in the learning framework and significantly improves its performance. In the application of ZA resolution in MT, it is viewed as a pre-processing module that is detachable and MT-independent. The experiment results demonstrate a significant improvement on BLEU/NIST scores after the ZA resolution is employed.