Paper: Entity Set Expansion using Topic information

ACL ID P11-2128
Title Entity Set Expansion using Topic information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

This paper proposes three modules based on latent topics of documents for alleviating “se- mantic drift” in bootstrapping entity set ex- pansion. These new modules are added to a discriminative bootstrapping algorithm to re- alize topic feature generation, negative exam- ple selection and entity candidate pruning. In this study, we model latent topics with LDA (Latent Dirichlet Allocation) in an unsuper- vised way. Experiments show that the accu- racy of the extracted entities is improved by 6.7 to 28.2% depending on the domain.