## 4 Experimental Section  
### Balanced Chinese Dataset Construction  
DuIEE2.0 [38] is the largest Chinese entity-relation extraction dataset, with 173,109 rows in the training set and 15,475 rows in the test set, including 49 entity-relation types. However, the number of occurrences of each relation in DuIEE2.0 is uneven and varies greatly, making it unsuitable for relation extraction experiments [39]. At the same time, the two special scenarios of SEO and EPO also account for a small percentage, making it unsuitable for demonstrating the advantages of REACT. For this reason, We optimized DuIEE2.0 by setting the threshold for the maximum number of occurrences of each relation in the training set to 230 and the threshold for each relation in the test set to 46. Table 1 shows some examples of sampling results where a few relation types fail to reach the threshold. To increase the number of data in the dataset containing EPO or SEO issues, we carefully collected over 1000 pieces of data, with each piece containing at least one type of overlapping problem. Figure 9 shows some of the data.  
Figure 8: Specific case of identifying the tail entity and relation based on the head entity.  
Finally, the high-quality Chinese dataset we constructed consists of 10,423 rows of training data, 2012 rows of test data, and 49 relation types. After optimization, the frequencies of various relations in the dataset were relatively uniform, covering multiple fields such as education, healthcare, finance, and law. The average length of all sentences is 56.28, with a total of 8043 different Chinese characters. Among the sentences in the dataset, 67% come from the Baidu Baike corpus, whereas 33% come from the Baidu News Push corpus. The distribution of different entity types is shown in Figure 10, with the most common types in the dataset being person, audiovisual work, song, and book. In the Chinese high-quality dataset we constructed, data with relation overlapping problems account for 32% of the total data.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Relation** & **DuIE2.0** & **Uniform** & & **Uniform** \\
**Number** & **Training Set** & **Sampling** & **DuIE2.0 Test Set** & **Sampling** \\
1 & 984 & 230 & 101 & 46 \\
2 & 3159 & 230 & 302 & 46 \\
3 & 1807 & 230 & 170 & 46 \\
4 & 7188 & 230 & 639 & 46 \\
5 & 8345 & 230 & 718 & 46 \\
6 & 593 & 230 & 46 & 46 \\
7 & 937 & 230 & 87 & 46 \\
8 & 1849 & 230 & 101 & 46 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
31 & 22 & 51 & 1 & 29 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & 46 \\
49 & 395 & 230 & 32 & 46 \\ Total number & 173,109 & 10,423 & 15,475 & 2012 \\ \hline \end{tabular}
\begin{tabular}{c|c|c|c} \hline
**Normal** & **Princess [PERSON]** was born during the late Sui Dynasty and early Tang Dynasty. Her **father** was none other than Emperor [PERSON] Tang, [PERSON], and she was his third daughter. \\ \hline
**SEO** & **According to reports from Japanese media citing Deadline, renowned Japanese horror film director [PERSON]** will serve as the producer of the movie **\"The Suicide Forest.\"** [PERSON] is best known for his direction of the classic horror film **\"The Gudge.\"** \\ \hline
**EPO** & **[PERSON]** finally returned to the stage of the Spring Festival Gala this year, performing a magic trick and singing the song **“Love Confession Balloon”** with magician [PERSON]. \\ \end{tabular}
\begin{tabular}{c|c|c|c} \hline
**Figure 9**: Data of Normal, SEO, and EPO classes in the high-quality Chinese dataset. \\ \end{tabular}
\end{table}
Table 1: Comparison between the optimized DuIE2.0 dataset and the original version.