## 1 Introduction  
Named entity recognition (NER) aims to identify words of specific meaning from text, e.g., person, organization, location, and is a fundamental task in natural language processing, aiming to identify the corresponding entities from text and label their types [1]. It is an essential human support task for downstream tasks such as syntactic analysis [2], machine translation [3], automated question and answer [4], and knowledge graphs [5]. The NER task was first proposed by [PERSON] et al. [6] and has been widely used in areas such as information extraction. Initially, lexicon-based and rule-based approaches were commonly used in NER, which mainly utilized string and model matching. [PERSON] et al. [7] proposed a method to better maintain rule-based NER and classification systems for improving the shortcomings in lexicon-based and rule-based approaches, but it still has some limitations. With the development of machine learning, statistical-based methods came into the public eye. [PERSON] et al. [8] were the first to add Conditional Random Fields (CRF) to NER and obtained good results, but the training time was too long. [PERSON] et al. [9] were the first to apply the maximum entropy model to NER, which combined a rule-based grammar with a maximum entropy model, but the time complexity required for training was high. In recent years, with the development of deep learning, deep-learning-based methods have also been applied to NER, eliminating the need for extensive manual feature engineering and domain knowledge provided by domain experts in traditional ways. [PERSON] et al. [10] combined Long Short-Term Memory (LSTM) with CRFs for the joint training of NER and word separation tasks, and the experimental results were effectively improved. In addition, methods based on convolutional neural networks [11] and hybrid neural networks [12] have also been widely used on the NER task with good results. The above methods are all oriented toward flat entities, which refer to entities that do not contain other entities within the identified entity. However, human languages are very complex, and not every token can be represented by a single label, so nested entities are prevalent in all areas of expertise [13]. A nested entity is a phenomenon where one or more short entities are contained within a long entity. As shown in Figure 1, using the above methods cannot identify the nested entities resulting in a loss of information. Here we map the cis-acting elements that mediate interleukin responsiveness of Protein  
the mouse IL-2R alpha gene using a thymic lymphoma-derived hybridoma  
To solve the nested entity problem, [PERSON] et al. [14] proposed a dynamic stacked entity decoding model, yet there is a phenomenon that the inner entities cannot use the information of the outer entities. Some researchers have tried to adopt a hypergraph-based approach [15; 16], but problems such as very complex hypergraph structures arise when sentences are too long or when there are too many classes of entities. Most researchers have used a span-based approach in model formation [17; 18] to solve the problem of nested entities. The span-based approach is used to divide the sentence into a two-dimensional grid, where each span in the grid is represented by the semantic information of the head and tail tokens, and the problem of nested entities can be naturally solved by classifying entities for each span. [PERSON] et al. [19] improved the span-based approach by locating entity boundaries and jointly learning the boundary detection and entity classification tasks. [PERSON] et al. [20] devised a span-based biaffine attention mechanism incorporating boundary information and used the biaffine mechanism to compute entity scores for NER. However, this approach does not consider the entity's local feature and the relative position feature between the head and tail tokens, which affects the performance of entity recognition.