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2006

Relation Extraction Using Label Propagation Based Semi-Supervised Learning

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Relation Extraction Using Label Propagation Based Semi-Supervised Learning
Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (LP) algorithm, for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. Experiment results on the ACE corpus showed that this LP algorithm achieves better performance than SVM when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task.
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu Niu
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