GraphIE: A Graph-Based Framework for Information Extraction

Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay

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.
Anthology ID:
N19-1082
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:
751–761
Language:
URL:
https://aclanthology.org/N19-1082
DOI:
10.18653/v1/N19-1082
Bibkey:
Cite (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.
Cite (Informal):
GraphIE: A Graph-Based Framework for Information Extraction (Qian et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/landing_page/N19-1082.pdf
Code
 thomas0809/GraphIE +  additional community code
Data
CoNLL 2003
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