Named Entity Recognition Based on Knowledge Fusion and Boundary Feature Sharing

Named entity recognition is a core task in the field of information extraction, which aims to recognize a relevant entity from unstructured text and classify that entity. Aiming at the problem that existing nested named entity recognition models cannot achieve better compatibility in recognizing long entities and the performance of the models, this study proposes a nested named entity recognition model based on knowledge fusion and boundary feature sharing. First, a joint training network based on the concepts of lexicon and knowledge graph is constructed to improve the text feature representation to obtain richer contextual information; second, the bimodal attention mechanism is used to capture the semantic correlation between lexical items and generate the spanning semantic representation matrix; furthermore, the boundary feature sharing strategy is designed to improve the generalization ability of the model for the rough transition problem of entity spanning boundaries. To verify the effectiveness of the model, it is tested on three benchmark datasets, including ACE2004, ACE2005, and SciERC datasets. The experimental results show that the proposed model outperforms the corresponding baseline model in terms of entity recognition F1 index.