计算机科学 ›› 2012, Vol. 39 ›› Issue (5): 229-233.

• 人工智能 • 上一篇    下一篇

基于机器学习方法的事件指代消歧研究

张宁,孔芳,李培峰,朱巧明   

  1. (苏州大学计算机科学与技术学院 苏州 215006) (江苏省计算机信息处理技术重点实验室 苏州 215006)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Research of Event Anaphora Resolution Based on Machine Learning Approach

  • Online:2018-11-16 Published:2018-11-16

摘要: 与实体指代不同,事件指代其先行词候选是一个事件,与名词性的指代词具有完全不同的语义分类体系,因此适用于实体指代消歧的大多数特征都不能用于事件指代消歧。给出了一个基于机器学习方法的事件指代消歧平台,详细介绍了平台的实例生成和特征选择过程,给出了平台在OntoNotes3. 0语料上的事件指代消歧的结果,并对结果进行了分析。从实验结果可以看到,给出的平台获得了较好的召回率,但系统准确率需要进一步提升。

关键词: 事件指代消歧,机器学习方法,实例生成,特征选择

Abstract: In event anaphora resolution, the antecedent of the anaphor is an event and the anaphor is a noun phrase.They are parts of different semantic categorization systems. So most of features applied in entity anaphora resolution are not appropriate for event anaphora resolution. hhis paper proposed an event anaphora resolution framework using a machine learning approach. The instances creation and the features selection were presented in detail. This paper also illustrated the experiment results on OntoNotes 3. 0 corpus. From the results we can find that the recall of the framework is very good, but the precision must be improved in the further work.

Key words: Event anaphora resolution, Machine learning approach, Instances creation, Features selection

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