@inproceedings{qian-etal-2019-graphie,
title = "{G}raph{IE}: A Graph-Based Framework for Information Extraction",
author = "Qian, Yujie and
Santus, Enrico and
Jin, Zhijing and
Guo, Jiang and
Barzilay, Regina",
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/N19-1082",
doi = "10.18653/v1/N19-1082",
pages = "751--761",
abstract = "Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks {---} namely textual, social media and visual information extraction {---} shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.",
}
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<abstract>Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks — namely textual, social media and visual information extraction — shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.</abstract>
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%0 Conference Proceedings
%T GraphIE: A Graph-Based Framework for Information Extraction
%A Qian, Yujie
%A Santus, Enrico
%A Jin, Zhijing
%A Guo, Jiang
%A Barzilay, Regina
%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 qian-etal-2019-graphie
%X Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks — namely textual, social media and visual information extraction — shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.
%R 10.18653/v1/N19-1082
%U https://aclanthology.org/N19-1082
%U https://doi.org/10.18653/v1/N19-1082
%P 751-761
Markdown (Informal)
[GraphIE: A Graph-Based Framework for Information Extraction](https://aclanthology.org/N19-1082) (Qian et al., NAACL 2019)
ACL
- Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, and Regina Barzilay. 2019. GraphIE: A Graph-Based Framework for Information Extraction. 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 751–761, Minneapolis, Minnesota. Association for Computational Linguistics.