Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

Pei, Shichao; Yu, Lu; Zhang, Xiangliang

Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

Type

Conference Paper

Authors

Pei, Shichao
Yu, Lu
Zhang, Xiangliang

KAUST Department

Computational Bioscience Research Center (CBRC)
Computer Science
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab

Date

2022-08-02

Abstract

Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.

Citation

Pei, S., Yu, L., & Zhang, X. (2022). Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract). 2022 IEEE 38th International Conference on Data Engineering (ICDE). https://doi.org/10.1109/icde53745.2022.00141