Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Kernel methods for relation extraction
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Authoring technical documents for effective retrieval
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
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To overcome the problem of not having enough manually labeled relation instances for supervised relation extraction methods, in this paper we propose a label propagation (LP) based semi-supervised learning algorithm for relation extraction task to learn from both labeled and unlabeled data. Evaluation on the ACE corpus showed when only a few labeled examples are available, our LP based relation extraction can achieve better performance than SVM and another bootstrapping method.