Multi-instance multi-label learning for relation extraction

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

Volumn , Issue , 2012, Pages 455-465

  Surdeanu, Mihai ^a   Tibshirani, Julie ^a   Nallapati, Ramesh ^b   Manning, Christopher D ^a  

^a STANFORD UNIVERSITY   (United States)

^b SRI INTERNATIONAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHICAL MODEL; LATENT VARIABLE; LEARNING SCENARIOS; MULTI-INSTANCE MULTI-LABEL LEARNING; RELATION EXTRACTION; TRAINING DATA;

EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84883402091     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Kedar Bellare and Andrew McCallum. 2007. Learning extractors from unlabeled text using relevant databases. In Proceedings of the Sixth International Workshop on Information Extraction on the Web.
2. Carla Brodley and Mark Friedl. 1999. Identifying mis-labeled training data. Journal of Artificial Intelligence Research (JAIR).
3. Razvan Bunescu and Raymond Mooney. 2007. Learning to extract relations from the web using minimal supervision. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics.
4. Mark Craven and Johan Kumlien. 1999. Constructing biological knowledge bases by extracting information from text sources. In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology.
5. Jenny Rose Finkel, Trond Grenager, and Christopher D. Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics.
6. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S. Weld. 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
7. Heng Ji, Ralph Grishman, Hoa T. Dang, Kira Griffitt, and Joe Ellis. 2010. Overview of the TAC 2010 knowledge base population track. In Proceedings of the Text Analytics Conference.
8. Heng Ji, Ralph Grishman, and Hoa T. Dang. 2011. Overview of the TAC 2011 knowledge base population track. In Proceedings of the Text Analytics Conference.
9. Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics.
10. Truc Vien T. Nguyen and Alessandro Moschitti. 2011. End-to-end relation extraction using distant supervision from external semantic repositories. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.
11. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD '10).
12. Ang Sun, Ralph Grishman, Wei Xu, and Bonan Min. 2011. New York University 2011 system for KBP slot filling. In Proceedings of the Text Analytics Conference.
13. Mihai Surdeanu, Sonal Gupta, John Bauer, David McClosky, Angel X. Chang, Valentin I. Spitkovsky, and Christopher D. Manning. 2011a. Stanford's distantly-supervised slot-filling system. In Proceedings of the Text Analytics Conference.
14. Mihai Surdeanu, David McClosky, Mason R. Smith, Andrey Gusev, and Christopher D. Manning. 2011b. Customizing an information extraction system to a new domain. In Proceedings of the Workshop on Relational Models of Semantics, Portland, Oregon, June.
15. Fei Wu and Dan Weld. 2007. Autonomously semanti-fying Wikipedia. In Proceedings of the International Conference on Information and Knowledge Management (CIKM).
16. Z.H. Zhou and M.L. Zhang. 2007. Multi-instance multi-label learning with application to scene classification. In Advances in Neural Information Processing Systems (NIPS).