Mention-centered Graph Neural Network for Document-level Relation Extraction

• URL: http://arxiv.org/abs/2103.08200v1
• Date: Mon, 15 Mar 2021 08:19:44 GMT
• Title: Mention-centered Graph Neural Network for Document-level Relation Extraction
• Authors: Jiaxin Pan, Min Peng, Yiyan Zhang
• Abstract summary: We build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Experiments show the connections between different mentions are crucial to document-level relation extraction.
• Score: 2.724649366608364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations.

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