Paper: Pattern Learning for Relation Extraction with a Hierarchical Topic Model

ACL ID P12-2011
Title Pattern Learning for Relation Extraction with a Hierarchical Topic Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state on- tological relations. We leverage distant su- pervision using relations from the knowledge base FreeBase, but do not require any man- ual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good preci- sion.