Paper: Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction

ACL ID C14-1199
Title Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Distantly supervised relation extraction, which can automatically generate training data by align- ing facts in the existing knowledge bases to text, has gained much attention. Previous work used conjunction features with coarse entity types consisting of only four types to train their model- s. Entity types are important indicators for a specific relation, for example, if the types of two entities are ?PERSON? and ?FILM? respectively, then there is more likely a ?DirectorOf? rela- tion between the two entities. However, the coarse entity types are not sufficient to capture the constraints of a relation between entities. In this paper, we propose a novel method to explore fine-grained entity type constraints, and we study a series of methods to integrate the constraints with the relation e...