摘要
事件抽取是信息抽取领域一个重要的研究方向,本文对事件抽取的两项关键技术——事件类别识别以及事件元素识别进行了深入研究。在事件类别识别阶段,本文采用了一种基于触发词扩展和二元分类相结合的方法;在事件元素识别阶段,本文采用了基于最大熵的多元分类的方法。这些方法很好的解决了事件抽取中训练实例正反例不平衡以及数据稀疏问题,取得了较好的系统性能。
Event Extraction is an important research point in the area of Information Extraction. This paper makes an intensive study of the two stages of Chinese event extraction, namely event type recognition and event argument recognition. A novel method combining event trigger expansion and a binary classifier is presented in the step of event type recognition while in the step of argument recognition, one with multi class classification based on maximum entropy is introduced. The above methods solved the data unbalanced problem in training model and the data sparseness problem brought by the small set of training data effectively, and finally our event extraction system achieved a better performance.
出处
《中文信息学报》
CSCD
北大核心
2008年第1期3-8,共6页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目(60575042
60675034)
国家863资助项目(2006AA01Z145)
关键词
计算机应用
中文信息处理
事件抽取
事件类别识别
事件元素识别
computer application
Chinese information processing
event extraction
event type recognition
eventargument recognition
