# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Experimental Results and Analysis
As the interaction rounds increase, the impact of increasing the pre-coefficient k on the model's F1 value decreases after 3 to 4 rounds. Although fluctuations still occur, the range of the model's F1 value is within a smaller interval. Furthermore, as the interaction rounds continue to increase, an excessive interaction causes the feature values to be too large, which reduces the model's accuracy. When the interaction round is 3 and the pre-coefficient is 1.327, the model's F1 value is 0.869, which is the best case in the statistics. When the interaction round is 4 and the pre-coefficient is 1.665, the model's F1  
Figure 3: **(a)** The impact of the leading coefficient k on the model’s accuracy with one round of interaction; **(b)** the impact of the leading coefficient k on the model’s accuracy with two rounds of interaction; **(c)** the impact of the leading coefficient k on the model’s accuracy with three rounds of interaction; **(d)** the impact of the leading coefficient k on the model’s accuracy with four rounds of interaction. value is 0.84, which is the best case in the statistics. It can be seen that adding an additional gated interaction mechanism can have a certain impact on the model's accuracy. Additionally, to test the performance of the BERT-MogrifierBiGRU-CRF network combined with the gated interaction unit, we conducted comparative experiments on the CLUENER2020 dataset using BERT-BiGRU-CRF, BERT-MogrifierBiLSTM-CRF, and the proposed BERT-MogrifierBiGRU-CRF network model in terms of loss values. The results of the comparative experiments on different models for the CLUENER2020 dataset are shown in Figure 4.  
From the experimental results, it can be observed that the introduction of the gated interaction mechanism significantly improves the performance on the dataset compared to the standalone BiGRU network. Both network models show faster convergence speeds and lower loss values in processing Chinese text data than those without the gated interaction mechanism. When comparing BERT Mogrifier BiLSTM CRF and the proposed BERT Mogrifier BiGRU CRF network model, the change from the long short-term memory network (LSTM) to the gated recurrent unit (GRU) further accelerates the model's convergence speed. This demonstrates that the gated interaction unit proposed in this paper exhibits a faster reduction in neural network loss values when processing Chinese text data and also shows higher sensitivity to feature changes in Chinese text data. To evaluate the performance and generalization capabilities of the model proposed in this study for Chinese named entity recognition (NER), its accuracy, recall, and F1-score metrics were compared with several other models on two public datasets: CCKS2019 and CLUENER2020. For the comparison on the public dataset CCKS2019, the experimental results are shown in Table 2.  
In this experiment, we compared the training times of BERT-BiGRU-CRF, BERT-BiLSTM-CRF, and the model proposed in this paper. The experimental results show that the training time for the BERT-BiGRU-CRF model is 175.25 min, the training time for the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **F1** & \(\mathbf{p}\) & **R** \\ \hline \multirow{3}{*}{CCKS2019} & Bert-mogrifierBiGRU-crf & 84.11 & 84.05 & 84.17 \\  & Bert-BiLSTM-crf & 82.67 & 82.35 & 83.01 \\  & Bert-BiGRU-crf & 83.13 & 82.94 & 83.33 \\ \hline \multirow{3}{*}{CLUENER2020} & Bert-mogrifierBiGRU-crf & 85.42 & 87.17 & 84.12 \\  & Bert-BiLSTM-crf & 79.69 & 84.47 & 76.54 \\ \cline{1-1}  & Bert-BiGRU-crf & 84.20 & 85.91 & 82.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the model on the CCKS2019 and CLUENER2020 dataset. Figure 4: Loss function trend chart. BERT-BiLSTM-CRF model is 186.32 min, and the training time for the model in this paper is 183.38 min. This indicates that using BiGRU instead of BiLSTM can effectively shorten the training time of the model.