A Joint Entity and Relation Extraction Model Driven by Fine-Grained Feature Extraction

Joint entity and relation extraction plays a crucial role in various fields, including natural language processing, knowledge graph construction, and question answering systems. Among these methods, the joint extraction approach based on table filling has become a focal point for researchers due to its excellent performance and ability to accurately extract entities and relations from intricate sentences. Despite this, there is still potential to be further explored in such methods. Currently, most methods focus on learning relational features between words but neglect the relational features between labels and between labels and words. These relations are also important because a relation triplet can only be correctly identified when all labels are predicted correctly. This some what increases the risk of missing key information and inefficient decoding in the entity and relation extraction process. To overcome this limitation, we design a fine-grained relation feature extraction module that aims to encourage the model to fully consider the associations between words and labels, as well as between labels, while focusing on the relationships between words. Specifically, we create label representations and embed them into fine-grained relation extraction, enabling it to treat labels and word representations as queries, keys, and values, and then utilize the self-attention mechanism to model the associations between them. Additionally, the table labeling strategy has a significant impact on model performance, especially in terms of decoding efficiency. We propose a vertex labeling method to label the table, enhancing the model’s accuracy in entity recognition and overall decoding efficiency. We evaluate our proposed model on three benchmark datasets. The experimental results demonstrate that our model is effective and achieves state-of-the-art performance on all these datasets.