该文提出了一种基于卷积树核的无指导中文实体关系抽取方法。该方法以最短路径包含树作为关系实例的结构化表示形式,以卷积树核函数作为树相似度计算方法,并采用分层聚类方法进行无指导中文实体关系抽取。在ACE RDC 2005中文基准语料库上的无指导关系抽取实验表明,采用该方法的F值最高可达到60.1,这说明基于卷积树核的无指导中文实体关系抽取是行之有效的。
Abstract
This paper proposes a convolution tree kernelbased approach for unsupervised Chinese entity relation extraction. This method first represents potential relation instances as shortest path-enclosed trees, then computes similarities between them using convolution tree kernel, finally groups them into various clusters through hierarchical clustering algorithms. Evaluation on the ACE RDC 2005 benchmark corpus shows that the convolution tree kernel-based approach achieves the highest F-measure of 60.1 on the task of unsupervised Chinese entity relation extraction, suggesting that this method is promising.
Key wordscomputer application; Chinese information processing; entity relation extraction; unsupervised learning; convolution tree kernel
关键词
计算机应用 /
中文信息处理 /
实体关系抽取 /
卷积树核 /
无指导学习 /
层次聚类
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Key words
computer application /
Chinese information processing /
entity relation extraction /
unsupervised learning /
convolution tree kernel
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参考文献
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脚注
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基金
国家自然科学基金资助项目(60873150,60970056,90920004);江苏省自然科学基金资助项目(BK2008160)
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