Joint entity and relation extraction with fusion of multi-feature semantics

Entity relation extraction is a key technology for extracting structured information from unstructured text and serves as the foundation for building large-scale knowledge graphs. Current joint entity relation extraction methods primarily focus on improving the recognition of overlapping triplets to enhance the overall performance of the model. However, the model still faces numerous challenges in managing intra-triplet and inter-triplet interactions, expanding the breadth of semantic encoding, and reducing information redundancy during the extraction process. These issues make it challenging for the model to achieve satisfactory performance in both normal and overlapping triple extraction. To address these challenges, this study proposes a comprehensive prediction network that includes multi-feature semantic fusion. We have developed a semantic fusion module that integrates entity mask embedding sequences, which enhance connections between entities, and context embedding sequences that provide richer semantic information, to enhance inter-triplet interactions and expand semantic encoding. Subsequently, using a parallel decoder to simultaneously generate a set of triplets, improving the interaction between them. Additionally, we utilize an entity mask sequence to finely prune these triplets, optimizing the final set of triplets. Experimental results on the publicly available datasets NYT and WebNLG demonstrate that, with BERT as the encoder, our model outperforms the baseline model in terms of accuracy and F1 score.