@article{Shen_Lyu_Ren_Vanni_Sadler_Han_2019, title={Mining Entity Synonyms with Efficient Neural Set Generation}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/3792}, DOI={10.1609/aaai.v33i01.3301249}, abstractNote={<p>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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shen, Jiaming and Lyu, Ruiliang and Ren, Xiang and Vanni, Michelle and Sadler, Brian and Han, Jiawei}, year={2019}, month={Jul.}, pages={249-256} }