# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 4 Experimental Results and Analysis
### Baselines  
To validate the performance of the MPSCER-NER model proposed in this study, comparative experiments were conducted on CoNLL-2003, Ontonotes 5.0, and Ontonotes 4.0 with benchmark models, including the following:  
1. [noitemsep]
2. NNshot and StructShot [27] comprise a simple NER based on nearest neighbor learning and structured reasoning. This is a supervised NER model trained on the source domain, which is used for feature extraction; a nearest neighbor classifier is used to learn in the feature space, capturing label dependencies between entity labels. 3. MatchingCNN [28] is a network that maps a small labeled support set; an unlabeled example for its label was proposed. It calculates the similarity between query instances and support instances, adapting to the recognition of new class types. 4. ProtoBERT [29] uses a token-level prototypical network that represents each class by averaging token representations with the same label; then, the label of each token in the query set is decided by its nearest class prototype. 5. DemonstrationNER [11] is a prompt learning NER method based on demonstration. The sentences marked in the dataset are selected as prompts to be input into the BERT model. The authors presented a demonstration of the relationship between the entities and the labels after the example sentences were constructed. This process helps the model to learn the contextual information from the task demonstration, contextualizing the task before the input, and enabling the model to recognize more entities through a good demonstration. 6. SR-Demonstration [30] is an NER method that was proposed for marking the relevance of demonstrations; it removes useless information from demonstration prompts, creates a relevance vocabulary consisting of tokens that appear in the annotated datasets, samples the tokens from the relevance vocabulary to replace the tokens in the demonstration, and calculates the most suitable demonstration sentence length required to achieve a demonstration of NER. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k\)-Shot & **Module Number** & _Precision_ & _Recall_ & **F1** \\ \hline  & 1 & 51.22 & 46.64 & 48.75 \\  & 2 & 66.51 & 47.92 & 48.98 \\
25-shot & 3 & 47.11 & 49.13 & 48.28 \\  & 4 & 51.75 & 50.11 & 50.75 \\  & 5 & 52.61 & 50.44 & 51.50 \\ \hline  & 1 & 65.92 & 62.51 & 63.61 \\  & 2 & 66.11 & 63.91 & 64.36 \\
50-shot & 3 & 65.36 & 64.92 & 65.18 \\  & 4 & 66.11 & 68.13 & 66.96 \\  & 5 & 66.89 & 68.85 & 67.55 \\ \hline  & 1 & 71.95 & 73.53 & 72.44 \\  & 2 & 72.11 & 73.82 & 72.91 \\
100-shot & 3 & 73.31 & 74.66 & 73.92 \\  & 4 & 73.53 & 75.94 & 74.63 \\  & 5 & 73.92 & 76.15 & 74.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of ablation experiments with the MPSCER-NER model. As can be seen from Table 8, the MPSCER-NER model shows better performances in the 25-shot and 50-shot scenarios on all three datasets--CoNLL-2003, Ontonotes 5.0, and Ontonotes 4.0--in comparison with DemonstrationNER (which performed better with the CoNLL-2003 dataset in terms of NNN shot, StructShot, MatchingCNN, ProtoBERT, DemonstrationNER, and SR-Demonstration). The MPSCER-NER model improved the \(F1\)-score of the 25-shot and 50-shot scenarios by 1.32% and 2.14%, respectively. Compared to SR-Demonstration--which had a better performance on Ontonotes 4.0 and Ontonotes 5.0--MPSCER-NER improved the \(F1\)-score of the 25-shot and 50-shot scenarios by 1.05%, 1.32%, 0.84%, and 1.46%. Compared to SR-Demonstration--which had a better performance on BC5 CDR--MPSCER-NER improved the \(F1\)-score in the 25-shot and 50-shot scenarios by 1.43% and 1.11%, respectively. The MPSCER-NER model was used for demonstration, identifying multi-class label prompts in the set of examples. Entity context links between the multi-class label prompts and the sentences that are to be recognized can be formed. The word vector representation in the sentence was optimized to make it closer to its corresponding label.