Combining linguistic features with weighted Bayesian classifier for temporal reference processing

Authors:
Guihong Cao;Wenjie Li;Kam-Fai Wong;Chunfa Yuan
Affiliations:
The Hong Kong Polytechnic University, Hong Kong;The Hong Kong Polytechnic University, Hong Kong;The Chinese University of Hong Kong, Hong Kong;Tsinghua University, Beijing, China
Venue:
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Year:
2004

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Abstract

Temporal reference is an issue of determining how events relate to one another. Determining temporal relations relies on the combination of the information, which is explicit or implicit in a language. This paper reports a computational model for determining temporal relations in Chinese. The model takes into account the effects of linguistic features, such as tense/aspect, temporal connectives, and discourse structures, and makes use of the fact that events are represented in different temporal structures. A machine learning approach, Weighted Bayesian Classifier, is developed to map their combined effects to the corresponding relations. An empirical study is conducted to investigate different combination methods, including lexicalbased, grammatical-based, and role-based methods. When used in combination, the weights of the features may not be equal. Incorporating with an optimization algorithm, the weights are fine tuned and the improvement is remarkable.