# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 4 Experimental Results and Analysis
### Ablation Studies  
The MPSCER-NER model is mainly divided into modules for multi-class label prompt selection and core entity replacement. Different combinations of the stages are used in the CoNLL-2003 dataset. The MPSCER-NER model is experimentally analyzed using the ablation method. The ablation experiments verified the validity of the multi-class label prompt selection and core entity replacement methods. The approach for selecting the ablation experiment modules is shown in Table 6.  
As can be seen from Table 6, Module 1 denotes the strategy comprising multi-class label prompt demonstration only. Module 2 denotes the strategy comprising multi-class label prompt demonstration with low-density core entity selection. Module 3 denotes the strategy comprising core entity replacement with a weighted random algorithm. Module 4 denotes the strategy comprising multi-class label prompt demonstration with core entity replacing. Module 5 is the model presented in this study, MPSCER-NER.  
From the results achieved using Module 1 and Module 2 in Table 7, it can be seen that the multi-class label prompt with a low-density core entity have improved the effects of the 25-shot, 50-shot, and 100-shot circumstances compared to the normal multi-class label prompts. Multi-class label prompts with low-density core entities have a clearer sentence structure. The entity context information between the sentence to be recognized and the demonstration sentence is clearer. As can be seen in Module 1, the _Recall_ of the entity recognition process is effectively improved through the use of the core entity replacement methods. As the demonstration prompt changes, the word vectors in the training sentences also change. The model is exposed to more word vectors and becomes more capable of recognizing entities. In 25-shot, 50-shot, and 100-shot circumstances, the MPSCER-NER model works optimally in terms of _Precision_, _Recall_, and \(F\)1-score. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module Number**} & \multirow{2}{*}{**Module**} & \multicolumn{2}{c}{**Multi-Class Label**} & **Core Entity** & **Low Core Entity** \\  & & **Prompt Demonstration** & **Replacing** & **Density Selecting** \\ \hline
1 & MPD & \(\surd\) & & & \\
2 & MPD + CEDS & \(\surd\) & & \(\surd\) \\
3 & CER & & \(\surd\) & \\
4 & MPD + CER & \(\surd\) & \(\surd\) & \\
5 & MPSCER-NER & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: MPSCER-NER model ablation experiment module selection.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**5-Shot**} & \multicolumn{4}{c}{**10-Shot**} \\  & **TP** & **TN** & **FN** & **FP** & **TP** & **TN** & **FN** & **FP** \\ \hline PER & 2389 & 36,628 & 317 & 129 & 2563 & 36,602 & 203 & 95 \\ LOC & 1185 & 37,059 & 738 & 481 & 1467 & 36,902 & 456 & 638 \\ ORG & 1935 & 35,502 & 560 & 1466 & 1874 & 36,267 & 621 & 701 \\ MISC & 247 & 38,017 & 671 & 528 & 546 & 37,919 & 372 & 626 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix on CoNLL-2003 (core entity replacement).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**5-Shot**} & \multicolumn{4}{c}{**10-Shot**} \\  & **TP** & **TN** & **FN** & **FP** & **TP** & **TN** & **FN** & **FP** \\ \hline PER & 2479 & 36,638 & 287 & 59 & 2654 & 36,625 & 112 & 72 \\ LOC & 1255 & 37,059 & 668 & 481 & 1581 & 36,858 & 342 & 682 \\ ORG & 1975 & 35,502 & 520 & 1466 & 1979 & 36,230 & 516 & 738 \\ MISC & 263 & 38,017 & 655 & 528 & 557 & 37,909 & 361 & 636 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix on CoNLL-2003 (MPSCER-NER).  
core entity replacement method for normal multi-class label prompts shows a sub-optimal performance. The results for this method prove that the multi-class label prompt selection and core entity replacement method can effectively improve the model's ability to recognize entities and that the effects can mutually enhance each other.