# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 2 Related Work  
Entity recognition methods can be categorized into three types: sequence labeling-based methods, hypergraph-based methods, and span-based methods. Sequence labeling-based methods assign labels to each character in the text to predict the entities and their categories. Common sequence labeling methods include \"BIO\" and \"BMES\" methods. For example, in the \"BIO\" labeling method, \"B-Category\" represents the beginning character of an entity, \"I-Category\" represents the internal characters of an entity, and \"O\" represents non-entity characters. Early sequence labeling methods assigned only one label to each character, making it impossible to assign corresponding labels to nested entities. To address this issue, researchers have proposed multi-label sequence labeling methods that allow a character to have multiple entity labels. These methods recognize nested entities through the multi-label annotations of characters. To enhance the capability of sequence labeling methods in nested entity recognition, researchers have proposed three strategies: hierarchical, cascaded, and structured (joint labeling) labeling approaches [9]. The hierarchical approach overlays a flat entity recognition layer based on the hierarchical information of nested entity structures, and multiple hierarchical entity recognition layers are used to recognize nested entities within entities. [PERSON] et al. [10] dynamically stacked multiple flattened feature extraction layers (BiLSTM+CRF) in the decoding part of entity recognition, and the study uses a hierarchical entity recognition model to recognize nested entities in biomedical texts. However, this stacking approach may introduce error propagation in the model structure, and the data in the model can only be transmitted in one direction, making it unable to utilize the information of outer entities when recognizing inner entities. [PERSON] et al. [11] proposed a pyramid NER model, which is also constructed based on the hierarchical approach. This model first stacks characters towards entity mentions from bottom to top to obtain the forward pyramid structure of entity mentions, which represents all possible entity fragments in the text. Then, the model decodes the forward pyramid in a reverse hierarchical manner to recognize all nested entities in the text. The cascaded approach builds a binary classifier for each entity category to predict nested entities and their categories. [PERSON] et al. [12] viewed the label sequence of nested entities as the second-best path within the long entity scope, and iteratively decoded the path from outer to inner layers to extract inner entities. This model used a single-layer CRF as the classifier for each cascade, and the CRF layer predicted the entity category. This  
classifies all possible entity boundaries using a final classifier. [PERSON] et al. (2020) used a head-driven phrase structure for nested named entity recognition. [PERSON] et al. (2021) proposed an MGNER neural network model that predicts each possible entity boundary, extracts all nested entities, and then classifies the recognized entities using a classification network module. While these methods can recognize nested entities, they have drawbacks such as high computational cost, insensitivity to boundary information, underutilization of partially matching entity boundary information, and difficulty in identifying entities in long texts. To address these issues, [PERSON] et al. (2022) divided the entity recognition task into two stages: entity boundary filtering and entity category prediction. In the first stage, this method extracts the boundaries of nested entities through filtering and boundary regression of seed spans. In the second stage, the extracted nested entities are classified. This method effectively utilizes the boundary information of entities and partially matching spans during the training process through these two stages. The span-based approach can also be implemented using pointer networks.