# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Experimental Results and Analysis  
To analyze the changes brought about by the introduction of the gated recurrent unit (GRU) and the gated interaction mechanism separately, this section follows the principle of controlling a single variable. First, experiments are conducted to compare the gated interaction mechanism, followed by a comprehensive comparison analysis with the GRU on this basis. The experiments in this section use the BERT-BiLSTM-CRF model as the baseline and set up the following model structures for comparison to analyze the performance of the GRU and gated interaction mechanism:  
1. BERT-BiLSTM-CRF: The baseline model structure, widely used in various tasks, with stable performance [34]. 2. BERT-BiGRU-CRF: Replaces the long short-term memory (LSTM) units with GRU units on the basis of the baseline model for comparison [35]. 3. BERT-Mogrifier-BiGRU-CRF: A model structure that combines the GRU and the gated interaction mechanism, an improvement over model (2). This section will first verify the applicability of the gated interaction mechanism and then compare the network structure of model (2) with other models on the CLUENER2020 dataset to analyze the overall performance of the improved model. To ensure the reliability of the experimental structure, the above model structures maintain consistency in some parameters with the same structure. Data Comparison Experiment and Result Analysis  
Using the Bayesian optimization algorithm described in Section 2, we selected the optimal hyperparameters for the gated interaction unit. When the round i is 3 and the pre-coefficient k is 1.32, the network is the optimal model. The table below lists the F1 evaluation values corresponding to different hyperparameters. To verify the impact of the interaction rounds r between GRU input and hidden layers on Chinese named entity recognition, we conducted experiments with different r values. With I and k as the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Batch\_Size** & **lr** & **Epoch** & **Embeding\_Dim** & **Hidden\_Dim** & **Dropout** \\ \hline Setting & 64 & 2e-5 & 30 & 768 & 256 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings. basic parameters and other parameters unchanged, the table shows that the variation of hyperparameters of the gated interaction unit has an important impact on the recognition results of the model. Using adaptive parameter tuning can better capture the relationship between parameters and recognition results, thereby selecting the most suitable parameters. The experimental results for the practicality of the gated interaction mechanism are shown in the following Figure 3.  
In the experimental results, we have statistically analyzed the impact of varying the pre-coefficient k on the F1 value at different interaction rounds, ranging from 1 to 4. The horizontal axis in the figure represents the pre-coefficient k of the interaction unit, while the vertical axis represents the harmonic mean of the model's precision and recall. Since the change in the pre-coefficient k is nonlinear, the results in the figure are presented using a scatter plot. From the figure, it can be observed that at different interaction rounds, changing the pre-coefficient k can have a certain impact on the model's accuracy. Specifically, when the pre-coefficient is too low, it can cause a severe decrease in the model's F1 value. However, when the value of the pre-coefficient is appropriately chosen, it can improve the model's accuracy. According to the experimental results, when the interaction rounds are between 1 and 2, the model's F1 value shows a relatively obvious fluctuating distribution as the pre-coefficient k increases. Specifically, when the interaction round is 1 and the pre-coefficient is 2.313, the model's F1 value reaches 0.81, which is the best case in the statistics. When the interaction round is 2 and the pre-coefficient is 1.801, the model's F1 value is 0.838, which is the best case in the statistics.