Exploring syntactic structured features over parse trees for relation extraction using kernel methods
Exploring syntactic structured features over parse trees for relation extraction using kernel methods
复制标题
使用核方法探索解析树上的句法结构化特征以进行关系提取
DOI：
10.1016/j.ipm.2007.07.013
复制
发表时间：
2008-03-01
影响因子：
8.6
通讯作者：
Aiti, Aw
中科院分区：
文献类型：
--
作者：
Min, Zhang;GuoDong, Zhou;Aiti, Aw
从文本文档中提取实体之间的语义关系是信息提取中的一个挑战，对于深度信息处理和管理具有重要意义。本文提出利用句法树上的卷积核与支持向量机相结合的方法对句法结构信息进行建模，用于关系抽取。与线性核相比，树核可以有效地挖掘隐含在句法分析树中的巨大句法结构特征。我们的研究表明，嵌入到句法树中的句法结构特征在关系提取中是非常有效的，并且能够被卷积树核很好地捕获。在ACE基准语料库上的评估表明，仅使用卷积树核可以获得与以前报道最好的基于特征的方法相当的性能。实验还表明，在关系抽取方面，我们的方法明显优于前两个依赖树核函数。此外，本文将卷积树核和简单的线性核相结合，提出了一种用于关系抽取的复合核。我们的研究表明，复合核可以有效地捕捉平面特征和结构化特征，而不需要进行大量的特征工程，并且可以很容易地扩展以包含更多的特征。在ACE基准语料库上的测试表明，复合核在关系抽取方面优于以往报道最好的方法。(C)2007爱思唯尔有限公司。保留所有权利。
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction. (c) 2007 Elsevier Ltd. All rights reserved.