# Prompt-Based Word-Level Information Injection BERT for Chinese Named Entity Recognition
## 4 Results
### Datasets
#### 4.1.5 Effectiveness Study  
We also compare the results on each category to analyze the influence of category information. WII-BERT denotes using the original Transformer structure before and after WILa. \"CONT\", \"RACE\", and \"LOC\" on the Resume dataset, and \"GPE.NOM\" are dropped while the F1 score becomes 100.00% or 0.00%. From Tables 5-8, the improvement occurs in most of the categories, and only four of them obtain an unexpected decrease. On Resume and MSRA datasets, PWII-BERT breaks down the effect for different categories and obtains an enhancement after adding the prompt-based category information. On the Ontonotes dataset, the category of \"GPE\", the effect is reduced after adding the category prompt. However, PWII-BERT achieves similar results with the other, with only a 0.05% gap. Due to the imbalance of training set and the limited training samples in the Weibo dataset, only part of the categories obtain a boost in the F1 score. In addition, more than half of the categories achieved better results. It can be observed that PWII-BERT does not specifically enhance the categories with a stable pattern, which proves that prompt-based category information improves the ability of the model to perceive the category from the perspective of the NER task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PWII-BERT**} & \multicolumn{3}{c}{**WII-BERT**} \\  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline GPE.NAM & 78.18 & 91.49 & 84.31 & 68.25 & 91.49 & 78.18 \\ LOC.NAM & 64.29 & 47.37 & 54.55 & 52.33 & 42.11 & 47.06 \\ LOC.NOM & 33.33 & 22.22 & 26.67 & 25.00 & 11.11 & 15.38 \\ PER.NAM & 79.28 & 79.28 & 79.28 & 74.58 & 79.28 & 76.86 \\ PER.NOM & 72.00 & 74.12 & 73.04 & 71.11 & 75.29 & 73.14 \\ ORG.NAM & 58.33 & 53.85 & 56.00 & 64.52 & 51.28 & 57.14 \\ ORG.NOM & 56.25 & 52.94 & 54.55 & 60.00 & 52.94 & 56.25 \\ Overall & 72.15 & 71.98 & 72.07 & 69.72 & 71.74 & 70.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of P, R, and F1 score on the Weibo dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weibo** & **Ontonotes** & **MSRA** & **Resume** \\ \hline PWII-BERT & 72.06 & 81.64 & 95.70 & 96.19 \\ w/o CRF & 71.79 & 81.23 & 95.45 & 96.13 \\ w/o prompt & 70.71 & 80.99 & 95.33 & 95.12 \\ w/o WIIA & 71.10 & 80.66 & 95.35 & 95.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation Study on Weibo, Ontonotes, MSRA, and Resume datasets. Deletion of CRF denotes using softmax as a sequence labeling decoder instead of CRF, deletion of prompt denotes using the original Transformer structure before and after WIIA, and deletion of WIIA denotes removing the WIIA and only using the prompt-based Transformer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PWII-BERT**} & \multicolumn{3}{c}{**WII-BERT**} \\  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline ORG & 93.36 & 96.56 & 94.93 & 93.25 & 94.94 & 94.09 \\ TITLE & 96.23 & 96.23 & 96.23 & 93.99 & 95.45 & 94.72 \\ EDU & 97.35 & 98.21 & 97.78 & 98.20 & 97.32 & 97.76 \\ Overall & 95.52 & 96.87 & 96.19 & 94.54 & 95.70 & 95.12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of P, R, and F1 score on the Resume dataset.