Abstract
Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.- Anthology ID:
- P19-1525
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5308–5314
- Language:
- URL:
- https://aclanthology.org/P19-1525
- DOI:
- 10.18653/v1/P19-1525
- Cite (ACL):
- Kalpit Dixit and Yaser Al-Onaizan. 2019. Span-Level Model for Relation Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5308–5314, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Span-Level Model for Relation Extraction (Dixit & Al-Onaizan, ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-1525.pdf
- Data
- ACE 2005