Mining Entity Synonyms with Efficient Neural Set Generation


  • Jiaming Shen University of Illinois at Urbana-Champaign
  • Ruiliang Lyu Shanghai Jiao Tong University
  • Xiang Ren University of Southern California
  • Michelle Vanni U.S. Army Research Laboratory
  • Brian Sadler U.S. Army Research Laboratory
  • Jiawei Han University of Illinois at Urbana-Champaign



Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.




How to Cite

Shen, J., Lyu, R., Ren, X., Vanni, M., Sadler, B., & Han, J. (2019). Mining Entity Synonyms with Efficient Neural Set Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 249-256.



AAAI Technical Track: AI and the Web