Document-level relation extraction aims to extract the relationship among the entities in a paragraph of text. Compared with sentence-level, the text in document-level relation extraction is much longer and contains many more entities. It makes the document-level relation extraction a harder task. The number and complexity of entities make it necessary to provide enough information about the entities for the models in document-level relation extraction. To solve this problem, we put forward a document-level entity mask method with type information (DEMMT), which masks each mention of the entities by special tokens. By using this entity mask method, the model can accurately obtain every mention and type of the entities. Based on DEMMT, we propose a BERT-based one-pass model, through which we can predict the relationships among the entities by processing the text once. We test the proposed model on the DocRED dataset, which is a large scale open-domain document-level relation extraction dataset. The results on the manually annotated part of DocRED show that our approach obtains 6% F1 improvement compared with the state-of-the-art models that do not use pre-trained models and has 2% F1 improvement than BERT which does not use the DEMMT. On the distant supervision generated part of DocRED, the improvement of F1 is 2% compared with no pre-trained models, and 5% compared with pure BERT.

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