## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 Overall Performance  
In this study, we propose a named entity recognition approach based on rule templates and Bert-Bi-LSTM-CRF. We contrast our approach with the generic entity recognition model and the deep-learning-based entity recognition model. Examples of traditional general models include the LTP model and the HanLP model. Since both of these models make use of probability and statistical concepts, such as the hidden Markov and cascade hidden Markov equations, they may be able to produce the final identification result without training. Deep-learning-based named entity models often employ the LSTM, Bi-LSTM, and Bi-LSTM-CRF models. The deep learning model's input is character embedding, and training the model is required before achieving entity recognition. The same experimental corpus was used for all trials. Only three categories of entities could be identified by the conventional general model: people's names, places, and organizations. The corpus contains no entities with people's names. In contrast to the conventional model, only the two categories of place and organization are counted. Entity recognition is deemed effective if the result is included into the labeled entity. The comparison of entity recognition performance among the model RT-BBC proposed in this paper, LTP [49], HanLP [50], LatticeLSTM [51], LexiconAugmentedNER [52], and ME-CNER [53] is shown in Table 3.  
From Table 3, it is clear that RT-BBC has high F1 values for place and organization entities, demonstrating high sensitivity to place and organization entities. The effectiveness of the traditional model's entity recognition is dependent on the primary job of natural language processing. The outcomes of parts-of-speech tagging and word segmentation have a direct impact on how accurately entities are recognized. Instead of relying on the outcomes of upstream tasks, the model in this study directly inputs the entire sentence to gather feature information, boosting organizational entity recognition performance in the process. The efficiency of RT-BBC is further demonstrated by the fact that, when compared to the other three deep learning methods, we outperformed them in all three measures. The recognition performances for the nine categories in the corpus are shown in Table 4. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Version** & **Network Layer** & **Number of Hidden Units** & **Head** & **Total Parameters** \\ \hline BERT-Base & 12 & 768 & 12 & 110 M \\ BERT-Large & 24 & 1024 & 16 & 340 M \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters of the two BERT models. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Place**} & \multicolumn{4}{c}{**Organization**} & \multicolumn{2}{c}{**Overall**} \\ \cline{2-9}
**Model** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline LTP & 42.15\% & 41.83\% & 41.99\% & 44.00\% & 9.50\% & 15.63\% & - & - & - \\ HanLP & 71.48\% & 57.52\% & 63.75\% & 73.43\% & 44.08\% & 55.09\% & - & - & - \\ LatticeLSTM & 73.33\% & 70.59\% & 71.93\% & 74.00\% & 62.36\% & 67.68\% & 70.97\% & 69.97\% & 70.47\% \\ LexiconAugmentedNER & 74.05\% & **73.26\%** & **73.66\%** & 68.07\% & 63.48\% & 65.70\% & 70.83\% & 69.26\% & 70.04\% \\ ME-CNER & 72.89\% & 64.71\% & 68.56\% & 59.78\% & 60.45\% & 60.11\% & 63.59\% & 65.15\% & 64.35\% \\ RT-BBC (ours) & **81.37\%** & 66.53\% & 73.20\% & **77.33\%** & **74.30\%** & **75.78\%** & **77.57\%** & **71.30\%** & **73.83\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results. The average F1 value for the four categories of entities based on rules is better than that of the LTP model and that of the HanLP model, was 86.23%, as can be seen in Table 4. The rate of date entity recognition was 97.20%. Date entities can be easily identified using the rule template method because of their high degree of regularity. The overall recognition performance of the method based on deep learning was poorer than that based on a rule template.