## 2 Related Works  
Traditionally, NER has been approached as a sequential labeling task. In this approach, tokens are annotated with BIO (Beginning, Inside, Outside) tags to identify entity boundaries and types. Various models based on conditional random fields (CRFs) have been developed to address this task [1]. In recent research, a machine reading comprehension model was utilized to recognize named entities by focusing on the part where NER was used to find a specific span [2]. Nested NER is challenging to perform using existing CRF-based models because it requires recognizing all nested named entities. In [3], based on the dependency parsing model [4], which utilizes a biaffine classifier to classify the dependency relationship between two words in a sentence, a biaffine Span classifier was applied to recognize nested named entities. In [5], a Tiaffine nested NER model was proposed by adding cross-span representation information to a Biaffine nested NER model. In [6], researchers attempted to handle nested NER using nested tags in traditional BIO tag-based CRF models. The study in [7] proposed a novel method called focusing, bridging, and prompting (FIT) for few-shot nested NER. This approach consists of three stages: a focusing stage that identifies entity-concentrated parts using pre-trained language models, a bridging stage for span extraction, and a prompting stage that utilizes contextual information and relationships between nested entities. It demonstrated superior performance compared to existing methods on four benchmark datasets: ACE 2004, ACE 2005, GENIA, and KBP2017. Ref. [8] introduced LPNER, a new label-prompt-based method for few-shot nested NER. This approach first extracts all possible spans from text and represents label information through an intuitive prompt template in the form of '[entity! type]'. The extracted spans are then classified by calculating similarity with prototype vectors for each entity type, enabling effective recognition of nested entities. Unlike existing multi-round prompt methods, it achieves computational efficiency through single-round processing and showed excellent performance on five nested NER datasets including ACE 2004, ACE 2005, and GENIA. Ref. [9] proposed a novel approach to nested NER using large language models (LLMs). The researchers found that decomposed-QA techniques, which were effective in flat NER, showed degraded performance in nested NER. They introduced inference techniques specialized for nested structures and instruction tuning utilizing detailed label information. While experiments on ACE 2004, ACE 2005, and GENIA datasets demonstrated the effectiveness of this approach, it fell short of BERT-based supervised learning models, suggesting the need for further research. Classifiers in traditional deep learning models attempt to classify input information by representing it in a vector space. Label embedding learns information regarding the label to be classified, represents it in a vector space, and uses it for classification purposes. The advantage of this label-embedding technique is that information about labels is learned simultaneously, making it possible to create a model that can handle data about labels that have not been learned at all [10]. In [11; 12], label-embedding technology was applied to CRF-based models for sequential labeling problems, and a label attention layer was used instead of a CRF layer. A model was proposed to utilize label information and encode it into an attention network. Ref. [13] proposed FewNER, a meta-learning-based model for few-shot NER. This method separates the network into task-independent and task-specific parts, where the task-independent part learns shared knowledge across multiple tasks through meta-learning, while the task-specific part is designed to adapt in low-dimensional space for each task.