Method for Relation Extraction Based on Entity Boundary Features

As a key task in natural language processing, the current knowledge extraction methods mostly involve joint extraction, simultaneously extracting named entities and relationships. When conducting relationship extraction, entities containing multiple words are usually represented using pooling or weighted sum methods, leading to the neglect of entity boundary information. To address the issue of reduced accuracy in relationship classification due to the introduction of intermediate noise information based on entity segment features, a relationship extraction method focusing on entity boundary features is proposed. Firstly, the semantic representation of the text is obtained using the pre-trained language model DeBERTa. Then, features of the head and tail positions of entities in the text are extracted and concatenated into a composite embedding vector containing information from these two key positions, used to enhance entity representation. By concatenating the composite embedding vectors of the head and tail entities, the final embedding vector of the entity pair is obtained, representing the information of each entity and enhancing the model's understanding of entity boundaries and semantic information. Finally, the existence of relationships between entity pairs is determined by the relationship discriminator and relationship classifier, simultaneously classifying the relationship types of the entity pairs for relationship classification. Experimental results show that the proposed method achieves F1 scores of 89.3%, 94.4%, 94.2%, and 94.9% on the NYT, NYT*, WebNLG, and WebNLG* datasets, respectively, significantly outperforming the baseline models. Additionally, ablation experiments demonstrate the importance of the relationship discriminator, further confirming the superiority of the relationship extraction method focusing on entity boundary features.