## 4 Experiments  
### Data and Evaluation Metrics  
In this research, in order to verify the effectiveness of the model proposed for Chinese entity relation extraction, experiments were conducted on the Chinese medical information extraction dataset CMeIE and the Baidu dataset DuIE2.0. The CMeIE dataset is a Chinese medical information extraction dataset released in CHIP2020 [40]. The dataset mainly focuses on pediatric diseases and one hundred common diseases, and it contains nearly 75,000 triples. The total number of training and validation sets is 17,924, which are randomly divided into training, validation, and test sets. In addition, the DuIE2.0 dataset was used to further validate the effectiveness of the model proposed in this research on other domain Chinese datasets. The dataset is from the 2020 Language and Intelligence Technology Competition, primarily sourced from Baidu Baike and Baidu News, which contains nearly 430,000 triples, 210,000 Chinese sentences, and 48 predefined relation types [41]. In order to see the allocation and share of different data more clearly, the specific statistical analysis of the two datasets is shown in Table 1. The approximate distribution is in the ratio of 6:2:2.  
Both datasets exhibit complex overlapping and nesting issues. Therefore, using these two datasets can effectively verify the model proposed in this research in addressing the issues of relation overlap and entity nesting. The overlapping triple types mainly include normal, SEO, and EPO, as well as cases of nested entities (subject object overlap, SOO). The number and ratios of the four complex data types for the two datasets are shown in Table 2. From the data, it can be seen that the normal and SEO types have the highest percentage of data, occupying a major part of the dataset. In contrast, the EPO and SOO types of data have a lower percentage, which undoubtedly increases the difficulty of training. Nonetheless, the performance of the model is inferior compared with normal and SEO types in identifying EPO- and SOO-type data. However, the model can still accurately identify EPO- and SOO-type data with a relatively small amount of data, which fully demonstrates the practicality and effectiveness of our model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Train (Ratio\%)** & **Test (Ratio\%)** & **Validation (Ratio\%)** & **All** & **Relation** \\ \hline DuIE2.0 & 137,258 (68\%) & 34,044 (17\%) & 30,485 (15\%) & 201,787 & 48 \\ CMeIE & 11,651 (65\%) & 3584 (20\%) & 2689 (15\%) & 17,924 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of the dataset.  
In this research, the performance of the constructed entity-relationship joint extraction model is evaluated with entity-relationship extraction evaluation metrics, including precision (P), recall (R), and F1 value. The formulas are as follows:  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{14}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{15}\]  
\[\text{F1}=\frac{2\text{PR}}{\text{P}+\text{R}} \tag{16}\]  
where TP denotes the number of correctly predicted triples, FP denotes the number of incorrectly predicted triples, and FN denotes the number of true triples not correctly predicted. The joint entity-relationship extraction model constructed in this research identifies a correctly predicted triad only if the head entity, relationship, and tail entity are all correctly predicted.