Document-Level Relation Extraction with Local Relation and Global Inference

The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serialization model method decreases the extraction accuracy as the text length increases. To address such problems, the goal of this paper is to develop a new approach for document-level relationship extraction by applying a new idea through the consideration of so-called "Local Relationship and Global Inference" (in short, LRGI), which means that we first encode the text using the BERT pre-training model to obtain a local relationship vector first by considering a local context pooling and bilinear group algorithm and then establishing a global inference mechanism based on Floyd's algorithm to achieve multi-path multi-hop inference and obtain the global inference vector, which allow us to extract multi-classified relationships with adaptive thresholding criteria. Taking the DocRED dataset as a testing set, the numerical results show that our proposed new approach (LRGI) in this paper achieves an accuracy of 0.73, and the value of F1 is 62.11, corresponding to 28% and 2% improvements by comparing with the classical document-level relationship extraction model (ATLOP), respectively.