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arXiv · 2103.08200

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

Jiaxin Pan·Min Peng·Yiyan Zhang
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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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Jiaxin Pan, Min Peng, Yiyan Zhang. 2021-03-15. Mention-centered Graph Neural Network for Document-level Relation Extraction. https://arxiv.org/abs/2103.08200

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