## 3 Results  
To evaluate the performance of the model, experiments were conducted on another data set with the same features. Here, we selected the data set the PFR People's Daily Corpus, whose scale was the same as that of the football data set, including one million characters. The entities in the public data set were classified into three categories: people's names (PER), places' names (LOC), and organizations' names (ORG). However, in our data set, entities were divided into five categories. The weighted test results with BIO annotation schemes are shown in Table 4. It can be seen from this table that the model achieved lower performance than those on \"The PFR People's Daily corpus\".  
We also compared the results of two different annotation methods BIO and BIOE, based on the ALBERT-BiLSTM model, as shown in Table 5. From the results, it can be seen that the BIOE annotation method had significantly higher accuracy, recall, and F-Score for all five categories of data. The BIOE tagging scheme was more suitable for the datasets and had better performance in identifying the boundaries of entities. Therefore, the BIOE annotation method was chosen for the subsequent analysis. We then compared the performance of the two models ALBERT-BiLSTM and ALBERT-BiLSTM-CRF in name entity recognition, and the results are shown in Figure 4. It is evident that the ALBERT-BiLSTM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **P (\%)** & **R (\%)** & **F-Score (\%)** \\ \hline Public Dataset & 93.46 & 89.23 & 91.26 \\ Football Dataset & 85.40 & 83.47 & 84.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The test results on “The PFR People’s Daily corpus” using BIO annotation schemes based on ALBERT-BiLSTM.  
model consistently exhibited higher precision, recall, and F-Score values across all entity categories when compared to ALBERT-BiLSTM-CRF. The F-Score of the ALBERT-BiLSTM model was 1.27% higher than that of the ALBERT-BiLSTM model.  
Additionally, we compared the results of previous studies on Chinese named recognition in football, which is shown in Table 6. We discovered that the authors used the most primitive named entity recognition method in the previous study, namely, rule based, which resulted in a low recall rate in the experimental results. The authors identified only three entity categories. From the comparison results, the model we adopted in football was more suitable for the downstream applications, relation extraction, information retrieval, event tracking, and intelligent question answering system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methodology** & **Dataset** & **Entity Category** & **P (\%)** & **R (\%)** & **F1-Score (\%)** \\ \hline Rule-based [46] & A total of 237 football transfer & 3 & 81.2 & 65.5 & 72.51 \\ ALBERT-BiLSTM & Our data set & 5 & 85.40 & 83.47 & 84.37 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the experiments between the current study and the one by [PERSON].  
Figure 4: Experimental results of five entity categories in ALBERT-BiLSTM and ALBERT-BiLSTM-CRF model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Annotation** & **Entity Category** & **P (\%)** & **R (\%)** & **F-Score (\%)** \\ \hline \multirow{4}{*}{BIO} & LOC & 80.96 & 72.45 & 76.47 \\  & ORG & 78.63 & 79.27 & 78.95 \\  & PER & 81.92 & 80.34 & 81.12 \\  & SCPT & 82.70 & 80.93 & 81.80 \\  & TERM & 63.83 & 52.29 & 57.49 \\  & LOC & 87.10 & 81.69 & 84.31 \\  & ORG & 86.27 & 86.74 & 86.50 \\ BIOE & PER & 86.02 & 84.08 & 85.04 \\  & SCPT & 85.59 & 85.74 & 85.66 \\  & TERM & 74.50 & 59.84 & 66.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The test results with different annotation schemes, based on ALBERT-BiLSTM.