Abstract
Many previous studies on relation extrac-tion have been focused on finding only one relation between two entities in a single sentence. However, we can easily find the fact that multiple entities exist in a single sentence and the entities form multiple relations. To resolve this prob-lem, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations by using a forward de-coder called an object decoder. Then, it finds 1-to-n subject-object relations by using a backward decoder called a sub-ject decoder. In the experiments with the ACE-05 dataset and the NYT dataset, the proposed model achieved the state-of-the-art performances (F1-score of 80.5% in the ACE-05 dataset, F1-score of 78.3% in the NYT dataset)- Anthology ID:
- D19-6608
- Volume:
- Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 47–51
- Language:
- URL:
- https://aclanthology.org/D19-6608
- DOI:
- 10.18653/v1/D19-6608
- Cite (ACL):
- Seong Sik Park and Harksoo Kim. 2019. Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 47–51, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism (Park & Kim, 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-6608.pdf
- Data
- ACE 2005