# Enhanced Chinese Domain Named Entity Recognition:
## 2 Related Work
### Prompt and Pointer Network  
Typically, there is not much annotated data for downstream tasks, and there is often a gap between pre-trained BERT models on large corpora and fine-tuning on downstream tasks. To address the gap, some researchers have proposed modeling downstream tasks in the form of pre-training tasks [29]. Taking BERT as an example, some tasks can be modeled as masked language modeling (MLM) or next sentence prediction (NSP), and this approach is particularly effective in scenarios with small datasets [30]. For instance, [PERSON] et al. [31] and others proposed the TemplateNER method, which completes NER using prompt learning. This method employs a template-based approach to solve the problem of few-shot NER: a template is pre-defined, for example, \"<candidate_span>is a <entity_type>entity\". With the help of a suitable prompt, this method reduces the difference between pre-training and fine-tuning, allowing the model to achieve good results with a small number of samples. It significantly outperforms traditional sequence labeling methods and distance-based few-shot NER methods in cross-domain and few-shot scenarios. Subsequently, methods based on the prompt mechanism have been widely used in NER [32; 33], such as modeling the task as machine reading comprehension and question answering (MRC-QA) format, fitting the NSP task for NER [34].  
The NER model based on the dual pointer network aims to address both the challenges of recognizing long entities and nested entities. [PERSON] et al. [35] proposed a unified extraction framework, where the NER module is based on a pre-trained model, employing a multi-layer dual pointer network. Input samples are constructed based on the template \"[SPOT]<entity_type>[text]\", predicting the start and end positions of possible entities in the samples, and NER is completed through the decoding algorithm. However, real-world datasets often exhibit a long-tail distribution problem, with a few types representing the majority of the samples. The multi-layer dual pointer network requires setting up a classifier for each entity type, potentially leading to some classifiers not being adequately trained, thus impacting the overall performance of NER model.  
To mitigate this issue, some researchers proposed a model based on a single-layer dual pointer network and the prompt mechanism. [PERSON] et al. [36] introduced a model based on a single-layer dual pointer network, constructing the input in the format \"entity_type>[SEP]<text>\" to accomplish NER. However, [PERSON] et al. [37] argue that the conventional design of the dual-pointer network, when performing NER or MRC, typically employs two modules to separately identify the start and end positions of entities. This can lead to inconsistencies during training and prediction. To address this issue, they proposed global pointer model which uses a globally normalized approach for NER. In non-nested scenarios, it achieves results comparable to CRF, and in nested scenarios, it performs well. Moreover, it allows for fully parallelized training, significantly accelerating the training process. This method addresses the inconsistency between training and prediction tasks from a model perspective.  
All these studies have explored NER methods based on pre-trained models and proposed unique insights and solutions for various issues. However, most of them optimized from the perspective of the model without considering the scarcity and differences in datasets across various domains in real-world applications. In contrast, this paper introduces a data augmentation method that enhances NER performance by increasing training data, alleviating the inconsistency between training and prediction tasks. This approach provides a reference for improving NER performance in domain-specific applications.