We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

GraphIE: A Graph-Based Framework for Information Extraction

lib:ad48d09c93a214d3 (v1.0.0)

Authors: Yujie Qian,Enrico Santus,Zhijing Jin,Jiang Guo,Regina Barzilay
Where published: NAACL 2019 6
ArXiv: 1810.13083
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1810.13083v3


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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!