结合句法结构化与平面特征的协同训练关系提取

2009 International Conference on Intelligent Networking and Collaborative Systems Pub Date : 2009-11-04 DOI:10.1109/INCOS.2009.15

Jing Qiu, L. Liao, Peng Li

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引用次数: 0

摘要

解析树包含了丰富的句法结构化信息，结构化特征已被证明是有效的关系提取方法。在本文中，我们提出了另一种以弱学习方式有效利用结构化特征的方法。我们选择了协同训练算法，将结构化特征设置为一个视图，将扁平特征设置为另一个视图。通过协同训练算法，我们可以将平面信息和结构化信息结合起来进行关系提取。

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Combining Syntactic Structured and Flat Features for Relation Extraction Using Co-training

A parse tree contains rich syntactic structured information, and the structured features have been proved effective in relation extraction. In this paper, we proposed another way to efficiently utilize structured features but in a weakly learning way. Co-training algorithm was chosen by us, the structured features were set to be one view of it, and the flat features were set to be the other. Through using co-training algorithm, we can combine both flat and structured information for relation extraction.

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2009 International Conference on Intelligent Networking and Collaborative Systems

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