## 2 Related Work  
NER is one of the critical steps in constructing a knowledge graph. In early methods, NER was treated as a sequence labeling task, often using Bidirectional Long Short-Term Memory (BiLSTM) [23] as the encoder and a Hidden Markov Model (HMM) [24] and CRFs as the decoders. [PERSON] et al. [25] proposed an improved HMM architecture, which  
Figure 1: Sentences with nested entities. includes using different observation features and introducing transition features to enhance the performance of NER. [PERSON] et al. [26] constructed a Czech NER system based on CRFs. Before the emergence of pre-trained language models, the BiLSTM-CRF model had achieved excellent performance in NER tasks. With the advent of pre-trained language models, combining them with traditional sequence labeling models can further enhance performance. However, the phenomenon of nested entities naturally exists in the text, in which a long entity contains one or more short entities. [PERSON] et al. [27] first proposed the concept of nested entities and constructed the biomedical nested entity recognition dataset, GENIA. Traditional sequence labeling methods cannot solve the problem of complex nested entities. Therefore, many researchers have explored ways for nested entity recognition. In the early stages, the layered-based model was mainly used for nested entity recognition. This model decodes nested entities by stacking multiple sequence labeling modules in a layered manner. For example, [PERSON] et al. [14] recognized nested entities from the inside out by stacking BiLSTM-CRF modules. The model first identifies the inner layer of entities. If there are entities in the current layer, the model stacks another BiLSTM-CRF module on top of the current one until all nested entities are recognized. By stacking CRF models, [PERSON] et al. [28] recognized nested entities in electronic medical records. While this approach is intuitive and easy to implement, the problem of error propagation becomes more pronounced as the number of nested entity layers increases. A transition-based model performs the sequential parsing of characters in the entire sentence to achieve nested entity recognition. [PERSON] et al. [29] introduced three components: Buffer, Stack, and Action. Buffer stores the unprocessed sentence, Stack stores the processed sentence, and Action processes the tokens in the sentence. Although transition-based methods do not suffer from error propagation, they have notable limitations. These methods can only handle entities consisting of two tokens; however, entities are often longer than that. The approach based on reading comprehension [30] cleverly transforms the named entity recognition task into a reading comprehension task. This approach converts each entity label into a question. For example, a question like \"What is the organization entity in this sentence?\" can be constructed for an organization entity. Then, the question and the target sentence are concatenated using a special token [SEP]. Finally, the entity is obtained based on the output position. However, this method lacks a fixed method for constructing questions, and the quality of the prediction heavily relies on the construction of the questions. To address the shortcomings of the previous methods, span-based methods [31] are now mainly used for nested entity recognition. The span-based approach classifies each sub-span in a sentence. For example, [PERSON] et al. [32] use a biaffine attention mechanism to generate a span representation of head and tail tokens for entity classification. However, this approach mainly considers only the head and tail tokens' information of entities to compose entities, resulting in the unsatisfactory performance of entity classification. Some work improves the performance of entity recognition by enhancing boundary information. For example, [PERSON] et al. [33] enhance the span representation by additional boundary supervision. [PERSON] et al.