Relation Extraction using Explicit Context Conditioning

Gaurav Singh, Parminder Bhatia


Abstract
Relation extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intra-sentence RE, and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times the target entities can be connected via a context token. We refer to such indirect relations as second-order relations, and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores to obtain final relation scores. Our empirical results show that the proposed method leads to state-of-the-art performance over two biomedical datasets.
Anthology ID:
N19-1147
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1442–1447
Language:
URL:
https://aclanthology.org/N19-1147
DOI:
10.18653/v1/N19-1147
Bibkey:
Cite (ACL):
Gaurav Singh and Parminder Bhatia. 2019. Relation Extraction using Explicit Context Conditioning. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1442–1447, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Relation Extraction using Explicit Context Conditioning (Singh & Bhatia, NAACL 2019)
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PDF:
https://aclanthology.org/N19-1147.pdf