Graphie: A graph-based framework for information extraction
Name
N19-1082.pdf
Description
Published version
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
df43871d5390bbd50a37c4f18c8c7ffc
Author(s)
Qian, Y
Santus, E
Jin, Z
Guo, J
Barzilay, R
Date Issued
June 2019
Journal
NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Citation
Qian, Y, Santus, E, Jin, Z, Guo, J and Barzilay, R. 2019. "Graphie: A graph-based framework for information extraction." NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1.
Version
Final published version
Abstract
© 2019 Association for Computational Linguistics 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/v1/N19-1082
