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 signifcant improvement on BLEU/NIST scores after the ZA resolution is employed.
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Peng, J., Araki, K. (2007). Zero Anaphora Resolution in Chinese and Its Application in Chinese-English Machine Translation. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds) Natural Language Processing and Information Systems. NLDB 2007. Lecture Notes in Computer Science, vol 4592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73351-5_32
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DOI: https://doi.org/10.1007/978-3-540-73351-5_32
Publisher Name: Springer, Berlin, Heidelberg
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