Graph-based semi-supervised relation extraction

被引：0

作者：

Chen, Jin-Xiu ^{[1
]}

Ji, Dong-Hong ^{[2
]}

机构：

[1] Department of Cognitive Science, Xiamen University

[2] Department of Computer Science, Wuhan University

来源：

Ruan Jian Xue Bao/Journal of Software | 2008年 / 19卷 / 11期

关键词：

Graph-based; Label propagation; Relation extraction; Semi-supervised learning;

D O I：

10.3724/sp.j.1001.2008.02843

中图分类号：

学科分类号：

摘要：

This paper investigates a graph-based semi-supervised learning algorithm, that is, label propagation algorithm for relation extraction. Labeled and unlabeled examples are represented as the nodes, and their distances as the weights of edges in the graph. The relation extraction tries to obtain a labeling function on this graph to satisfy the global consistency assumption. Experimental results on the ACE (automatic content extraction) corpus showed that this method achieves a better performance than SVM (support vector machine) when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task.

引用

页码：2843 / 2852

页数：9

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