Contrastive Learning Enhanced Graph Relation Representation for Document-level Relation Extraction

In the field of biomedicine, Document-level relation extraction (DocRE) aims to reason about complex relational facts among entities by reading, inferring, and aggregating among entities over multiple sentences in a document. Existing studies construct document-level graphs to enrich interactions between entities. However, these methods pay more attention to the entity nodes and their connections, regardless of the rich knowledge entailed in the original corpus. In this paper, we propose a contrastive learning enhanced document-level graph relation representation(CGDRE) which mines the semantic knowledge from the original corpus and improve the ability of DocRE. Firstly, we use a coreference contrastive learning module to capture the potential semantic knowledge. Secondly, we construct a heterogeneous graph to enhance the graph structure information according to the original document and semantic knowledge. Lastly, CGDRE infers relations on the aggregated graph and uses focal loss to train the model. Remarkably, it is amazing that CGDRE can effectively alleviate the long-tailed distribution problem in the DocRE. Experiments on the public datasets, CDR, GDA and DocRED, show that CGDRE can significantly outperform other baselines, achieving a significant performance improvement. Extensive analyses demonstrate that the performance of our CGDRE is contributed by the capture of the semantic knowledge enhanced graph relation representation.