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Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the whole document as an essential global feature while ignoring the discrimination of entity representation. Focusing on local semantic feature, a novel model named GCNLEF based on graph convolutional network is proposed in this paper. In the presented method, a heterogeneous graph containing mention nodes and sentence group nodes is constructed first. Then multi-hop path reasoning is presented to infer the relations between entities. Experimental results on DocRED show that the proposed model can achieve 61.45 F 1 score with 90 epochs, improved by 0.15 F 1 score compared with the state-of-the-art method ATLOP.

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