## 2 Related Work
[22] used transfer learning to bootstrap a neural network by feeding pre-trained word embedding to a secondary task on annotated electronic records, which achieved an F1-score of 94.7% on the I2B22009 Medical Information Extraction Challenge, 4.3% higher than the traditional method that initially won the challenge. The performance of models based on deep learning methods depends, to a large extent, on the number and quality of samples in labeled datasets, so researchers propose pre-trained language models, which generate representations with prior knowledge in a large-scale corpus, achieving new results on various NLP tasks. The pre-training models for entity recognition mainly include the ELMO model [23], the BERT model [24], and the OpenAI GPT model [25], where the most commonly used pre-training model is the BERT model. [PERSON] et al. [26] integrate the ELMO pre-trained model embedding into LSTM models and apply them to Chinese medical entity recognition tasks. Compared with other popular models, good performance was achieved on two datasets related to Chinese medical entities. [PERSON] et al. [27] took character-level features as input features, which were subsequently mapped to character embedding through an embedding layer using a bidirectional encoder representation from a transformer (BERT) model, a BiLSTM layer, and CRF was used to encode the character embedding and final output label. In many practical problems, unlabeled data are abundant, but labeled data are less. One of the ways to improve the performance of supervised learning models is to provide a large amount of labeled data, but labeling data often requires a lot of labor and time costs. In addition to the widespread existence and easy collection of unlabeled data, the most important thing is that no labeling is required. However, supervised learning algorithms cannot use unlabeled data, and it is difficult to achieve strong generalization performance when there are few labeled data samples. Although unsupervised learning can use unlabeled data samples, the accuracy of its models is generally poor. In response to the above problems, scholars have proposed semi-supervised learning. Semi-supervised learning [28] is a learning method that combines supervised learning and unsupervised learning, which can use both labeled and unlabeled data. At present, the self-training algorithm in the semi-supervised learning algorithm most commonly used in the field of medical entity recognition includes the Co-Training algorithm [29], Self-Training algorithm [30], and Tri-Training algorithm [31]. In addition to semi-supervised learning through these three algorithms, existing research also uses semi-supervised dictionary learning algorithms, label propagation algorithms, and semi-supervised learning algorithms based on graph theory. For example, [PERSON] [32] proposed an integrated semi-supervised algorithm based on the maximum probability voting scheme, and the experimental results show that the algorithm outperforms the classical semi-supervised algorithm in terms of classification accuracy. [PERSON] and [PERSON] [33]used a Self-Training algorithm in semi-supervised learning for the Chinese medical entity recognition task. Further, an integrated learning algorithm was used in the proposed model, and a classifier model based on the BiLSTM-CRF network model was employed, which achieved good performance in the relevant medical dataset. [PERSON] et al. [34] proposed a pseudo-label-based semi-supervised learning algorithm that uses pre-trained modules to filter out erroneous pseudo-labels to create high-quality labeled data, achieving comparable performance to state-of-the-art models on the CoNLL-2003 and OntoNotes5.0 English NER task. In this paper, we mainly develop semi-supervised learning through the Tri-Training algorithm and apply it to the Chinese medical entity recognition task.