Boundaries and edges rethinking: An end-to-end neural model for overlapping entity relation extraction. Issue 6 (November 2020)

• Record Type: Journal Article
• Authors: Fei, Hao; Ren, Yafeng; Ji, Donghong
• Is Part Of: Information processing & management. Volume 57:Issue 6(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Natural language processing -- Information extraction -- Neural networks -- Entity relation extraction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102311
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, we propose an end-to-end neural model for overlapping relation extraction by treating the task as a quintuple prediction problem. The proposed method first constructs the entity graphs by enumerating possible candidate spans, then models the relational graphs between entities via a graph attention model. Experimental results on five benchmark datasets show that the proposed model achieves the current best performance, outperforming previous methods and baseline systems by a large margin. Further analysis shows that our model can effectively capture the long-distance dependencies between entities in a long sentence.