Paper: Multi-instance Multi-label Learning for Relation Extraction

ACL ID D12-1042
Title Multi-instance Multi-label Learning for Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Distant supervision for relation extraction (RE) ? gathering training data by aligning a database of facts with text ? is an efficient ap- proach to scale RE to thousands of different relations. However, this introduces a challeng- ing learning scenario where the relation ex- pressed by a pair of entities found in a sen- tence is unknown. For example, a sentence containing Balzac and France may express BornIn or Died, an unknown relation, or no re- lation at all. Because of this, traditional super- vised learning, which assumes that each ex- ample 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...