Paper: Structured Relation Discovery using Generative Models

ACL ID D11-1135
Title Structured Relation Discovery using Generative Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We explore unsupervised approaches to rela- tion extraction between two named entities; for instance, the semantic bornIn relation be- tween a person and location entity. Con- cretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of ob- served triples of entity mention pairs and the surface syntactic dependency path between them. The output of each model is a cluster- ing of observed relation tuples and their as- sociated textual expressions to underlying se- mantic relation types. Our proposed models exploit entity type constraints within a relation as well as features on the dependency path be- tween entity mentions. We examine effective- ness of our approach via multiple evaluations and demonstrate 12% error reduc...