Combining linguistic features with weighted Bayesian classifier for temporal reference processing
Guihong Cao, Wenjie Li, Kam‐Fai Wong, Chunfa Yuan · 2004
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.