Double Graph Based Reasoning for Document-level Relation Extraction

• URL: http://arxiv.org/abs/2009.13752v1
• Date: Tue, 29 Sep 2020 03:41:01 GMT
• Title: Double Graph Based Reasoning for Document-level Relation Extraction
• Authors: Shuang Zeng, Runxin Xu, Baobao Chang and Lei Li
• Abstract summary: Document-level relation extraction aims to extract relations among entities within a document. We propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art.
• Score: 29.19714611415326
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entity-level graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/DreamInvoker/GAIN .

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