@inproceedings{singh-bhatia-2019-relation,
title = "Relation Extraction using Explicit Context Conditioning",
author = "Singh, Gaurav and
Bhatia, Parminder",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1147",
doi = "10.18653/v1/N19-1147",
pages = "1442--1447",
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.",
}
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%0 Conference Proceedings
%T Relation Extraction using Explicit Context Conditioning
%A Singh, Gaurav
%A Bhatia, Parminder
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F singh-bhatia-2019-relation
%X 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.
%R 10.18653/v1/N19-1147
%U https://aclanthology.org/N19-1147
%U https://doi.org/10.18653/v1/N19-1147
%P 1442-1447
Markdown (Informal)
[Relation Extraction using Explicit Context Conditioning](https://aclanthology.org/N19-1147) (Singh & Bhatia, NAACL 2019)
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.