Structured Relation Discovery using Generative Models

We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic bornIn relation between a person and location entity. Concretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. The output of each model is a clustering of observed relation tuples and their associated textual expressions to underlying semantic relation types. Our proposed models exploit entity type constraints within a relation as well as features on the dependency path between entity mentions. We examine effectiveness of our approach via multiple evaluations and demonstrate 12% error reduction in precision over a state-of-the-art weakly supervised baseline.

• Type: Journal
• Year: 2011
• Where: EMNLP
• Authors: Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew McCallum
• Keywords: EMNLP 2011, Natural Language Processing, Probabilistic Models, Semantic Relation, Type Constraints