@inproceedings{ji-etal-2020-span,
title = "Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations",
author = "Ji, Bin and
Yu, Jie and
Li, Shasha and
Ma, Jun and
Wu, Qingbo and
Tan, Yusong and
Liu, Huijun",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.8",
doi = "10.18653/v1/2020.coling-main.8",
pages = "88--99",
abstract = "Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.",
}
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<abstract>Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.</abstract>
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%0 Conference Proceedings
%T Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
%A Ji, Bin
%A Yu, Jie
%A Li, Shasha
%A Ma, Jun
%A Wu, Qingbo
%A Tan, Yusong
%A Liu, Huijun
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ji-etal-2020-span
%X Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.
%R 10.18653/v1/2020.coling-main.8
%U https://aclanthology.org/2020.coling-main.8
%U https://doi.org/10.18653/v1/2020.coling-main.8
%P 88-99
Markdown (Informal)
[Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations](https://aclanthology.org/2020.coling-main.8) (Ji et al., COLING 2020)
ACL