@inproceedings{dixit-al-onaizan-2019-span,
title = "Span-Level Model for Relation Extraction",
author = "Dixit, Kalpit and
Al-Onaizan, Yaser",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1525/",
doi = "10.18653/v1/P19-1525",
pages = "5308--5314",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Span-Level Model for Relation Extraction
%A Dixit, Kalpit
%A Al-Onaizan, Yaser
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F dixit-al-onaizan-2019-span
%X 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.
%R 10.18653/v1/P19-1525
%U https://aclanthology.org/P19-1525/
%U https://doi.org/10.18653/v1/P19-1525
%P 5308-5314
Markdown (Informal)
[Span-Level Model for Relation Extraction](https://aclanthology.org/P19-1525/) (Dixit & Al-Onaizan, ACL 2019)
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.