A Time-Event Mapping Method Based Transformation
Chun Yuan · Zhongwen xinxi xuebao · 2004
In the past years, temporal information processing and extraction has received increasing attentions. Nevertheless, only a few researchers have investigated the recognition about corresponding temporal expression of the event in Chinese text. The aim of this paper is to investigate both the temporal information extraction and the determining of mapping relation between event and its temporal expression. As compared to many other techniques, we use a machine learning method, transformation based error driven learning algorithm to determine the time event mapping relation. The method can automatically acquire the analytical rules. The system builds an initial time event tagger firstly. Then by machine learning, the system get a patch rule set to improve the performance of the initial time event tagger. Using the patch rule set, system gets 6.5% error rate decrease for time event mapping relation determination. The experiment indicates that the transformation based error driven learning is a good patch for based rule method.