## 4 Results and Analysis of the Experimental Results
### Mengzi-BiLSTM-Crf
#### 4.1.4 Experimental Result  
The experimental results in Table 3 show that the entity model proposed in the paper performs substantially better than the comparison models in the three datasets of Resume, Weibo, and MSRA in terms of comprehensive performance. The smaller the dataset is, the more obvious the effect improvement is.  
\begin{table}
\begin{tabular}{c c} \hline
**Operating System** & **Version** \\ \hline CPU & AMD Ryzen 5 5600X 6-Core Processor 3.70 GHz \\ \hline GPU & 3060 Ti \\ \hline Python & 3.6 \\ \hline Pytorch & 1.7.1 \\ \hline \end{tabular}
\end{table}
Table 1: Named entity recognition experimental environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **Max Length** & **Crf\_lr** & **Adapter\_lr** & **Epoch** & **Batch Size** & **Loss\_Type** & **Learning Rate** \\ \hline values & 150 & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & 20 & 12 & Ce & \(1\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 2: Named entity recognition experiment parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Resume** & **Weibo** & **MSRA** \\ \hline  & & & **F1** \\ \hline BERT & 95.33 & 67.27 & 94.71 \\ \hline ERNIE & 94.82 & 67.96 & 95.08 \\ \hline ChineseBERT & / & 70.80 & / \\ \hline MFE-NER & 95.73 & 67.74 & 89.96 \\ \hline LeBERT & 96.08 & 70.75 & 95.70 \\ \hline
**LeBert-Bilstm** & **96.61** & **73.72** & **95.86** \\ \hline \end{tabular}
\end{table}
Table 3: Experimental results’ F1 value in multiple Chinese named entity recognition datasets.  
The table shows the experimental results of multiple models on different Chinese named entity recognition datasets. As shown in the table, the F1 scores improve in all Three datasets, which indicates that combining word vectors and pinyin vectors to improve the accuracy of named entity recognition is effective. The first five rows list the variant models based on the BERT model, of which the first row is the BERT baseline model. The second row is Baidu's optimization model ERNIE based on the BERT model in April 2019 [25]. Next is ChineseBERT [26], which integrates the glyph information and pinyin information of Chinese characters. It integrates the connection between Chinese characters, glyphs, pronunciation, and context. The glyph vector is composed of several different structures, and the pinyin vector is obtained from the corresponding sequence of romanized pinyin characters. The two are fused together with the word vector to obtain the final fusion vector as the input to the pre-trained model. The fourth row follows a similar approach of merging glyph and pinyin, but MFE-NER employs a distinctive method for handling fonts. It captures glyph features and utilizes the \"Wubi\" encoding scheme to represent the structural patterns of Chinese characters, enhancing the proximity of characters with similar glyph structures in the embedding space. Lastly, the original LEBERT model incorporates lexical information into the model, enhancing named entity recognition capabilities. Building upon LEBERT, this study introduces modifications and compares the performance with several models, including BERT, ChineseBERT, and the enhanced LEBERT. The results demonstrate the efficacy of combining Chinese language models and dictionary features for Chinese named entity recognition. In this paper, we improve the LEBERT model based on it, compare it with several models for verification, and achieve better performance. These include BERT, ChineseBERT, and the improved LEBERT, which proves the effectiveness of combining Chinese speech models and lexical features for Chinese named entity recognition. Compared with the LEBERT model, this paper has a large improvement in entity recognition for different categories. The F1 values for entity recognition in the Weibo dataset for different categories are shown in Figure 6.  
As shown in Figure 6, the recognition effect of the proposed model in this paper is significantly improved in several entity classifications, especially for specific location names and specific organization names.