Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)
Type
Conference PaperKAUST 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
2022-08-02Permanent link to this record
http://hdl.handle.net/10754/680134
Metadata
Show full item recordAbstract
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/icde53745.2022.00141Publisher
IEEEConference/Event name
2022 IEEE 38th International Conference on Data Engineering (ICDE)ISBN
978-1-6654-0884-4Additional Links
https://ieeexplore.ieee.org/document/9835161/ae974a485f413a2113503eed53cd6c53
10.1109/icde53745.2022.00141