## 4 Experimental Section
### Model Variants and Ablation Experiments  
In order to comprehensively evaluate the performance of REACT and the effects of the proposed Entity Attention Mechanism (EAM) and Entity Gated Mechanism (EGM), we compared it with 14 variants, as shown in Table 4. We employed _Dictionary Encoding (DE)_,  
Figure 11: Comparison of convergence rates between REACT and the baselines. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline RSAN & 59.7 & 57.6 & 58.6 \\ CasRel & 65.7 & 64.2 & 64.9 \\ TPLinker & 65.3 & 66.4 & 65.8 \\ REACT & 68.5 & 66.0 & 67.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental data for REACT and the baselines on the high-quality Chinese dataset. BERT_, and _Roberta_ as feature extraction methods, and replaced the _BiLSTM_ in the Encoding layer with a _CNN_ and _LSTM_. The parameter settings for the Dictionary Encoding in the experiments were based on the work of [PERSON] et al. [42]. The parameter settings for _BERT_ were based on the work of [PERSON] et al. [32]. [PERSON] et al. [42] conducted a large number of experiments in the field of Chinese relation extraction, and their proposed model achieved good results. The CasRel model proposed by [PERSON] et al. [32] achieved state-of-the-art performance in 2020. Therefore, by referring to the above research to set model parameters, we can ensure the performance of the model. For _LSTM_ and _CNN_, we conducted experiments and fine-tuned them to select the optimal parameters, as shown in Table 2.  
As shown in Table 5, the best performance was achieved using the _BiLSTM_, and the worst performance was achieved using the _CNN_, regardless of which encoding method was used. The relation extraction task usually requires the analysis of the whole sentence and needs to be inferred in context. The _BiLSTM_ was able to perform forward and backward processing along the sentence sequence, thus capturing the long-term dependencies and context in the sentence, helping to better understand the semantics of the sentence and the relations between sentence components. _CNNs_ are mainly used to process fixed-size local features and have a relatively low ability to model long-distance dependencies in sentences. Furthermore, _CNNs_ are usually locally aware and do not have an explicit memory mechanism, thus may not be able to handle long-term dependencies and complex semantics in sentences. Based on the overall results, compared to using _Dictionary Encoding_ as the feature extraction method, using _BERT_ and _Roberta_ as the encoder was more effective, with their F1-scores both above 63%. Both _BERT_ and _Roberta_ are based on pre-trained language models that learn rich linguistic representations from large amounts of textual data through large-scale unsupervised learning. _BERT_ and _Roberta_ utilize the mechanism of parameter sharing to encode the individual words of an entire sentence at the same time. In contrast, _Dictionary Encoding_ usually only extracts features based on independent words and lacks modeling of the sentence as a whole. In addition, _BERT_ and _Roberta_ employ the Transformer, which is capable of bidirectional context modeling to better capture the semantics and context of words in a sentence or text. This synthesis of contextual information is essential for understanding and expressing the meaning of a text. _Dictionary Encoding_ usually performs feature extraction based on independent words or phrases and cannot capture the contextual information between words.