Paper: Relation Extraction Using Label Propagation Based Semi-Supervised Learning

ACL ID P06-1017
Title Relation Extraction Using Label Propagation Based Semi-Supervised Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning al- gorithm, a label propagation (LP) algo- rithm, 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 la- beling 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 bootstrap- ping for the relation extraction task.