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|Title:||Relation extraction using label propagation based semi-supervised learning||Authors:||Chen, J.
|Issue Date:||2006||Citation:||Chen, J.,Ji, D.,Tan, C.L.,Niu, Z. (2006). Relation extraction using label propagation based semi-supervised learning. COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference 1 : 129-136. ScholarBank@NUS Repository.||Abstract:||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. © 2006 Association for Computational Linguistics.||Source Title:||COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/41233||ISBN:||1932432655|
|Appears in Collections:||Staff Publications|
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