## 3 Model Structure
### Improvements to the Tri-Training Algorithm  
The Chinese medical entity recognition model based on the improved Tri-Training algorithm proposed in this paper is mainly semi-supervised learning by the Tri-Training algorithm. The original Tri-Training algorithm has certain limitations, so the following three aspects of improvement are made to improve the overall performance of the model. We change the way the original labeled training sample set constructs the sub-training sample set to get an even distribution. The original Tri-Training algorithm samples the original labeled data with Bootstrap Sample, which is a method for extracting the labeled training sample set with replacement to get three sub-training sample sets. On the one hand, this extraction method will lead to insufficient extraction of labeled samples resulting in the waste of labeled data. On the other hand, there may be a large amount of duplicate data  
Figure 1: BIBC model structure diagram. among the three sub-training sample sets extracted by replacement. Using these repeated training samples and the same structure of the base classifier, the trained base classifier model may output the same labeled results when facing the same sample instances due to its own model, thus interfering with the performance of the model. In this paper, the original labeled training sample set is used to construct the sub-training sample set, and then the original labeled training sample set is divided equally. Specifically, the original labeled training sample set is divided into three sub-training sample sets, which are used for training the three base classifiers. The base classifier selects three different neural network models. The original Tri-Training algorithm uses three base classifier models with the same structure. Although each base classifier uses different training data, the degree of discrimination between classifiers with the same structure is not high. It is easy to output the same wrong label when facing the same sample instance, which results in poor performance of the final integrated classifier. In view of this problem, the Chinese medical entity recognition model based on semi-supervised learning proposed in this paper adopts three deep neural network models as the network model of the base classifier. These three network models are the BERT-IDCNN-CRF model (BIC), BERT-BiLSTM-CRF model (BBC), and BERT-IDCNN-BiLSTM-CRF model (BIBC). Based on these three network models and three sub-training sample sets, three different base classifier models are trained. Ensemble learning adopts a voting rule that combines minority-over-majority voting rules and weighted voting. The original Tri-Training algorithm uses the majority-over-minority method. If the three base classifiers have different labels for a sample instance in the integration learning stage, they will be counted according to the error at this time, which results in poor performance of the final integrated classifier. In view of this problem, this paper proposes a voting rule that combines the minority-over-majority voting rule and weighted voting. The rules are as follows, the labeling results generated by the three base classifiers \(C_{1}\), \(C_{2}\), and \(C_{3}\) are analyzed, and if the labeling results of the three base classifiers are exactly the same, then the result is considered correct. We will select the minority-over-majority voting rule. If only two base classifiers mark the same result, then the two identical labeled results are considered correct. The weighted voting rule needs to be used for selection when the results marked by the three base classifiers are different. The rules of weighted voting are shown in Formula (1), where the performance weight of each classifier is considered during integration learning, and the weight is determined by the classification accuracy \(P_{i}(L_{i})\) of the three base classifiers on the initial labeled training dataset \(L\).