## 1 Introduction  
Named Entity Recognition (NER), as a prerequisite for information extraction, is a study aimed at annotating characters in text sequences and finding out entity types. NER as a foundational task was first proposed by Grishman et al. at the sixth in a series of Message Understanding Conferences (MUC-6) [1] and then expanded in MUC-7 [2] with four types of entities: time, date, percentage, and value. In the extraction process, researchers found that extracting these valuable entities can be of great use, and NER is the basic work for tasks such as question and answer systems and building knowledge graphs. In the medical NER task, the early approaches used are rule-based and lexicon-based approaches. That is, scholars in the related fields manually construct some rule templates to recognize entities in the text by pattern matching or string matching. In the medical field, the entities involved are mainly entity types such as drug names, disease names, drug doses, symptom names, etc. Since there are many fixed proper names in this field, researchers initially used a dictionary and rule-based approach in the medical field. As many of the rules used need to be developed by experienced experts, these efforts are quite costly and each domain has different rules that are difficult to transfer to other domains. Because of the capability to automatically learn text features of the deep learning models, deep learning based NER methods have become a hot research topic [3]. As a classical sequence labeling task, NER is a very fundamental technology in many high level NLP applications [4]. The deep learning based NER models mostly consist of three parts. The first one is the embedding stage. In this stage, we map words into distributed representations and a series of Pre-trained Language Models (PLMs) are proposed to learn contextual word representations. GPT [5] selects Transformer [6] to extract one-way text information and achieves great performance. In addition, BERT [7] has also achieved good results under many NLP tasks which uses CBOW to train a bidirectional language model and MLM to stochastically mask entities in text input. As Chinese has its unique characteristics that differ in various fields such as syntactic relations, semantic relations, and so on [8], scholars begin to build models that fit Chinese lingual characteristics [9]. Even though Li et al. [10] try to prove that tokenization in Chinese has relatively little effect, Sun et al. [11] propose ERNIE with three masking strategies to better capture multi-granularity semantics. In 2019, Cui et al. [12] propose BERT with whole word masking which mask all characters included in one Chinese word to adapt to the natural characteristics of Chinese. The second part is the context encoder module to extract sequence features and then capture the contextual dependencies of the input sequence. In order to make the future state also able to predict the current output, bidirectional RNNs such as Bi-LSTM [13] and Bi-GRU are proposed. According to the research of Liu et al. [14], LSTM has great potential for NER for clinical texts because it does not require hand-crafted features. However, the training speed of RNNs is limited by its temporal characteristics, and the advantages of convolutional kernel weight sharing used in CNN models can reduce the computational complexity and multiple convolutional kernels that can be computed in parallel are revalued by scholars [15;16]. The third and the last part, namely the inference module, takes representations from the second part and generate the optimal label to finish the process of NER. The Soft-max function is a generalization of the binary classification function Sigmoid for multi-classification tasks, aiming at presenting the results of multi-classification in the form of probabilities. As a linear classifier, it is a popular component of many models that treat the process of sequence labeling as a set of independent classification tasks. This leads to neglecting dependencies in nearby labels.