Span-Level Model for Relation Extraction

Kalpit Dixit, Yaser Al-Onaizan


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P19-1525.pdf
Data
ACE 2005