Research on Entity Pair Relation Classification Based on Contrastive Learning and Biaffine Model

In the field of natural language processing, Biaffine model is a typical neural network structure based on double affine transformation, which helps to understand sentence structure and the relationships between words, which can be used for tasks such as text classification and relation extraction. However, this model faces challenges in entity pair relation classification tasks, such as imbalanced relation types and unclear entity pair feature information. Therefore, this paper proposes an improved relation classification model named Bert-CL-Biaffine, which is based on bidirectional entity and contrastive learning, combining a global pointer network with contrastive learning to enhance the Biaffine model for relation classification tasks. By training the model to identify the start and end positions of entities in sentences, it performs better in classifying overlapping entity pairs in complex scenarios. Experimental results show that on the NYT and WebNLG datasets, the F1 score of Bert-CL-Biaffine model improves by 1% and 1.2%, respectively, compared to baseline models, indicating that the improved relation classification model effectively enhances performance in complex scenarios.