Entity Set Expansion based on Bootstrapping Methods using Topic Information

Abstract

This paper proposes three modules based on latent topics of documents for alleviating “semantic drift” in bootstrapping entity set expansion. These new modules are added to a discriminative bootstrapping algorithm to realize topic feature generation, negative example selection and positive example disambiguation. In this study, we model latent topics with LDA (Latent Dirichlet Allocation) in an unsupervised way. Experiments show that the accuracy of the extracted entities is improved by 6.7 to 28.2% depending on the domain.