Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel
Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel
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DOI：
10.3115/1220835.1220872
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
Min Zhang;Jie Zhang;Jian Su
中科院分区：
文献类型：
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作者：
Min Zhang;Jie Zhang;Jian Su
本文提出了使用卷积核的解析树模型的句法结构信息的关系提取。我们的研究表明，句法结构特征嵌入在一个解析树是非常有效的关系提取，这些功能可以很好地捕获的卷积树内核。ACE 2003语料库的评估表明，卷积核的解析树可以实现与以前最好的报告基于特征的方法的24 ACE关系子类型的性能相当。它还表明，我们的方法显着优于前两个依赖树内核上的5 ACE关系主要类型。
This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extraction. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extraction and these features can be well captured by the convolution tree kernel. Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable performance with the previous best-reported feature-based methods on the 24 ACE relation subtypes. It also shows that our method significantly outperforms the previous two dependency tree kernels on the 5 ACE relation major types.