# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer  
[PERSON]  
1 School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China; [EMAIL_ADDRESS] (J. Based on whether entities can overlap in the text, entity recognition can be divided into flat entity recognition and nested entity recognition. In flat entity recognition tasks, entities do not share any overlapping characters. Each characters is either an invalid character or belongs to a unique entity. For example, in the text \"[(\"Beijing\" has the Forbidden City\"), there are only two entities, \"[(\"Beijing\") and \"[(\"Forbidden City\"), and these two entities do not share any characters. Therefore, the entity recognition task for this text can be achieved through a flat entity recognition model. However, in reality, text context is complex, and there may exist nested entities within an entity. For example, in the text \"[(\"Peking University is located in Haidian District, Beijing\"), there are three entities, \"[(\"Peking University\"), \"[(\"Beijing\")), and \"[(\"Haidian District\"). In this case, the entity \"[(\"Bei-nested entity\"). Thus, in complex contexts, nested entities occur more frequently, and the entity recognition task for such texts cannot be achieved through a flat entity recognition model. To address this issue, nested entity recognition methods have been developed, allowing various types of entities to exist within an entity. For example, in the aforementioned text \"[(\"Peking University is located in Haidian District, Beijing\"), the organization entity \"[(\"Peking University\") contains the location entity \"[(\"Beijing\"). With the emergence of the neural network model AlexNet, deep learning gained significant popularity, and various classic neural network modules were proposed. Along with the popularity of deep learning, many researchers began to focus on deep learning-based models for natural language processing. NER systems based on deep learning have achieved remarkable performance improvement. Many studies have used deep learning models to enhance the feature extraction layer of NER models, allowing them to extract more semantic information from text. [PERSON] et al. [2] added an attention mechanism to improve the quality of semantic features. [PERSON] et al. [3] added deep belief network (DBN) to the entity recognition model, which improved the classification accuracy of the model. [PERSON] et al. [4] used the BILSTM-CRF model to realize entity recognition, and obtained text timing information through BILSTM. [PERSON] et al. [5] added a CNN module to improve the local feature extraction capability of the entity recognition model. Therefore, this paper adopts an entity recognition model based on deep learning to implement the nested entity recognition task. As the feature capacity of classical neural network models is insufficient, this paper constructs a deep semantic extraction module called IDCNNLR. To enhance feature quality, this paper incorporates multi-head self-attention mechanisms at multiple positions in the model. Finally, the model predicts nested entities and their categories through the GlobalPointer module. The paper proposes a Chinese nested entity recognition model called BERT-ATT-IDCNNLR-ATTR-GlobalPointer. Currently, the medical field contains a large amount of information, covering a wide range of content. It is necessary to perform the timely processing of disease, medication, and medical device information. [PERSON] et al. [6] realized entity recognition of electronic medical records through Multi-Neural Network Fusion. [PERSON] et al. [7] integrated a knowledge graph and attention mechanism into the entity recognition model to realize entity recognition of Internet medical consultation texts. [PERSON] et al. [8] used a pre-trained language model to implement entity recognition in clinical medical texts. In real life, text contexts are complex, and commonly used texts like medical data inevitably contain numerous nested entities.