Abstract
As a basic unit of knowledge representation and an important means for information organization, event has drawn growing number of people’s attention, the research of event identification and extraction in natural language processing field is an important research topic in information extraction area, the recognition and extraction of event trigger word plays a decisive role in event identification and extraction. In this paper, the authors make experiment in Chinese Event Corpus CEC, and present a method of extracting event trigger word automatically that combines extended trigger word table and machine learning. The experiment result shows that the F-score of extracting event trigger word. can reach 71.2% by using this method.
Highlights
The concept of “event” is widely discussed in philosophy, cognitive psychology and linguistics and other fields, people hope to know and understand the world by way of studying event and relationship between events
As a basic unit of knowledge representation and an important means for information organization, event has drawn growing number of people’s attention, the research of event identification and extraction in natural language processing field is an important research topic in information extraction area, the recognition and extraction of event trigger word plays a decisive role in event identification and extraction
Definition2-3 (Event Trigger Word) Event trigger word is defined as the word that expresses what happens in text[11]
Summary
The concept of “event” is widely discussed in philosophy, cognitive psychology and linguistics and other fields, people hope to know and understand the world by way of studying event and relationship between events. With the rapid development of the Internet, more and more people tend to get information that they are interested in from Internet, the study of event in natural language processing is quietly rising in this demand-driven. Because of the difference of annotation systems, the standard of extraction of event trigger word is not uniform, this phenomenon causes some chaos. For example,”离婚” is treated as trigger word of “离婚女连骗糊涂男” in ACE corpus, but most people think that the main thrust of this sentence is “骗”, so “离婚” can not be treated as trigger word. This chaos cause event annotation to be inconsistent. Experiment result shows that this method can effectively improve F-score of extracting work
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