Research on the identification method of Chinese medical named entities

Named Entity Recognition (NER) plays a crucial role in Chinese Natural Language Processing, particularly in medical texts containing numerous nested entities. Existing NER techniques often rely on Bidirectional Long Short-Term Memory networks combined with Conditional Random Fields (BiLSTM-CRF) to identify entities. However, their effectiveness in handling nested entities is limited. Therefore, this paper proposes a model based on multilevel convolutional networks and Biaffine networks. While single layer convolutional networks capture local features, they lack the ability to capture long-range dependencies in text. To address this issue, multilevel convolutional networks are utilized to integrate information from multiple scales. Subsequently, nested entities are identified using Biaffine networks. The proposed method demonstrates promising performance on various medical text entity recognition tasks, outperforming existing methods in nested entity recognition, as evidenced by experimental results.