## 2 Related Works
During testing, only the task-specific part is updated while keeping the task-independent part fixed, enabling efficient adaptation to new tasks and achieving superior performance in intra/cross-domain adaptation experiments compared to existing methods. The research in [14; 15] explored few-shot named entity recognition using label embedding. Ref. [14] proposed the SpanNER framework, which can perform effective named entity recognition with minimal training data. This method learns from natural language descriptions of entity classes and processes entity span detection and class inference in separate modules, attempting named entity recognition using word similarity between spans and natural language descriptions. It was specifically designed to enable zero-shot learning and achieved performance improvements of 10% in few-shot learning, 23% in domain transfer, and 26% in zero-shot learning across five datasets compared to existing methods. Ref. [15] attempted few-shot named entity recognition for transfer learning by creating embedding vectors from simple label words and using them for BIO tag-based named entity recognition classification. The model proposed in this paper uses label descriptions similar to the work of [14; 15] but uses label descriptions of one completed long sentence as label information, and the model in [14; 15] is a flat NER model, not nested NER, and uses label information in combination with input information; however, in this study, we use the Biaffine model as the base model for nested NER and label information only in label attention in the output layer. The model proposed in this paper uses a span-based affine nested NER model similar to that in [3] but utilizes label information in the output layer to show high performance in a few-shot learning environment. Our work makes several key contributions that differentiate it from previous approaches to nested NER in few-shot learning environments:  
* Unlike existing few-shot nested NER methods such as FIT [7], LPNER [8], and FewNER [13] that require complex multi-stage frameworks, our approach achieves comparable or superior performance through a more elegant solution--modifying only the classification layer with label semantic information. * While previous label-aware approaches like [14; 15] focus primarily on flat NER and combine label information with input features, our model specifically addresses nested NER challenges by integrating rich label descriptions directly into the output layer of a span-based architecture. * By utilizing comprehensive label descriptions rather than single words, our model captures deeper semantic relationships between entities, making it more robust in few-shot scenarios where limited examples are available. * Our method enables effective transfer learning across different domains and datasets, including between nested and flat NER tasks, demonstrating greater versatility than traditional classification-based approaches. * Extensive experiments across multiple datasets (GENIA, ACE 2004, ACE 2005, and CoNLL 2003) demonstrate that our approach maintains high performance across various entity types, including the challenging nested relationships, where previous methods often struggle in few-shot environments.