## 4 Experiments
### Experimental Results and Analysis  
Experimental parameter settings. In our experiments, there were some differences in core parameter settings between different models. The detailed description is shown in Table 3.  
In the experiment, considering that our machine configuration was limited, we set the maximum batch_size to 8. It is strongly recommended that that is updated according to the actual configuration of the machine being used. Results and Analysis. According to the introduction of the experimental design section, to evaluate different tasks, three groups of comparative experiments were carried out. For the NER task, we conducted \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corpus Language** & **Samples Size** & **Entity Categories** & **Entity Size** \\ \hline Chinese & 194,747 & 25 & 860,000+ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistical info of the Chinese Language and Smart Technology Information Extraction named entity recognition (#CLSTIE-NER) dataset. \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Model** & **Name** & **Value** & **Name** & **Value** \\ \hline \multirow{3}{*}{BERT-based Bi-LSTM-CRF task name} & max\_seq\_length & 128 & batch\_size & 4 \\  & learning\_rate & \(5\times 10^{-5}\) & num\_labels & 25 \\  & dropout\_keep\_prob & 0.5 & train\_epoch & 10 \\  & task\_name & NER & bert\_model\_dir & chinese\_L\_12\_H-768\_A-12 \\ \hline \multirow{3}{*}{BERT-based Att-Bi-GRU task name} & max\_seq\_length & 128 & batch\_size & 8 \\  & learning\_rate & \(2\times 10^{-5}\) & num\_relations & 33 \\ \cline{1-1}  & dropout\_keep\_prob & 0.5 & train\_epoch & 12 \\ \cline{1-1}  & task\_name & RC & bert\_model\_dir & chinese\_wwm\_ext\_L-12\_H-768\_A-12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The core parameters settings of fine-tuned BERT-based encoding model. relevant experiments on the #CLSTIE-NER dataset, and selected CRF [8] and Bi-LSTM-CRF [9] as the baseline models. The experimental results are shown in Table 4.  
Compared with the baseline models, the results in Table 4 show that our model was superior among all indicators, and the average Precision, Recall, and F\({}_{1}\)-score reached 90.51%, 85.27%, and 87.81%, respectively, on the experimental dataset. The performance of the CRF model was slightly worse, with an F\({}_{1}\)-score of 81.26%. For the specific relation prediction task, we conducted relevant experiments on the #RC dataset, and selected DepNN [12], Att-based Bi-LSTM [13], Att-based CNN [16], and BERT-based Att-Bi-LSTM as the baseline models. The experimental results are shown in Table 5.  
For the specific relation prediction task, the results in Table 5 show that our model was better than all baseline models. Relative to the DepNN method, our model indicators increased by more than 6%. After adding BERT encoding, the classification performance of the model was effectively improved. For the entire open relation prediction task, we conducted the experiment on the #ORP-Test dataset and selected ZORE [20], UnCORE [21], and DSNFs-based ORE [22] as the baseline models. The results are shown in Table 6.  
According to the results of Table 6, our model had a higher comprehensive performance than the other three baseline models. Precision and F\({}_{1}\)-score reached 84.74% and 71.2%, and were at least 1.07% higher than those of the baseline models. Case Analysis. Here, two simple cases are shown in Table 7 to illustrate our method. In Table 7, the predict entity list refers to the named entity recognition results obtained by the algorithm, and the target entity list refers to the results of human annotation. In addition, the predicted triples represent all triples predicted by the algorithm, and target triples represent the results of