Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Autonomously semantifying wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Knowledge-based weak supervision for information extraction of overlapping relations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
End-to-end relation extraction using distant supervision from external semantic repositories
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Customizing an information extraction system to a new domain
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
Feature-based models for improving the quality of noisy training data for relation extraction
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Extracting meronyms for a biology knowledge base using distant supervision
Proceedings of the 2013 workshop on Automated knowledge base construction
A survey of noise reduction methods for distant supervision
Proceedings of the 2013 workshop on Automated knowledge base construction
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Distant supervision for relation extraction (RE) -- gathering training data by aligning a database of facts with text -- is an efficient approach to scale RE to thousands of different relations. However, this introduces a challenging learning scenario where the relation expressed by a pair of entities found in a sentence is unknown. For example, a sentence containing Balzac and France may express BornIn or Died, an unknown relation, or no relation at all. Because of this, traditional supervised learning, which assumes that each example is explicitly mapped to a label, is not appropriate. We propose a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables. Our model performs competitively on two difficult domains.