Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 57, Issue 6, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2020.102311
Keywords
Natural language processing; Information extraction; Neural networks; Entity relation extraction
Funding
- National Natural Science Foundation of China [61702121, 61772378]
- Major Projects of the National Social Science Foundation of China [11ZD189]
- Research Foundation of Ministry of Education of China [18JZD015]
- Key Project of State Language Commission of China [ZDI135-112]
- Guangdong Basic and Applied Basic Research Foundation of China [2020A151501705]
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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.
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