Entity Set Expansion using Topic information
Kugatsu Sadamitsu, Kuniko Saito, Kenji Imamura, Genichiro Kikui · 2011
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 entity candidate pruning. 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. 1