# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 5 Conclusions  
Intelligent dialogue systems are gradually being integrated into people's lives. Dialogue state tracking is a fundamental task that intelligent dialogue systems must perform. NER provides entity information that helps dialogue state tracking models to accurately understand users' words. In this study, a multi-class label prompt selection and core entity replacement-based named entity recognition (MPSCER-NER) model was proposed. In the multi-class label prompt selection phase, multi-class label prompts from the instance dataset are selected as candidates. Low-density multi-class label candidate prompts are used as a demonstration; these are input into the model together with the sentences that are to be recognized. Contextual links between entities are established. The word vector representation is optimized to improve the model's ability to identify positive samples. In the core entity replacement phase, the weighted random algorithm is used to find out the core entities that will be replaced. Then, the word vectors used for the training data are enriched. The ability of the model to recognize entities in different scenarios can thus be enhanced. In the entity classification stage, BiLSTM is used to deal with word vectors with information from multi-class label prompts. CRF is used to deal with the problem of mismatch between the fronts and the backs of the labels. The experimental results show that, on CoNLL-2003, Ontonotes 5.0, Ontonotes 4.0, and BC5 CDR under 5-Way \(k\) Shot (\(k=5\), 10), the MPSCER-NER model achieves minimum \(F1\)-score improvements of 1.32%, 2.14%, 1.05%, 1.32%, 0.84%, 1.46%, 1.43%, and 1.11% in comparison with NNNshot, StructShot, MatchingCNN, ProtoBERT, DNER, and SRNER, respectively.These results demonstrate the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**CoNLL-2003**} & \multicolumn{2}{c}{**Ontonotes 5.0**} & \multicolumn{2}{c}{**Ontonotes 4.0**} & \multicolumn{2}{c}{**BC5 CDR**} \\  & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** & **25-Shot** & **50-Shot** \\ \hline NNNshot & 51.43 & 62.45 & 40.11 & 56.17 & 41.23 & 55.18 & 48.71 & 50.13 \\ StructShot & 50.23 & 63.67 & 41.49 & 58.09 & 42.34 & 58.46 & 49.33 & 51.94 \\ MatchingCNN & 50.23 & 63.67 & 41.49 & 58.09 & 42.34 & 58.46 & 49.87 & 51.28 \\ ProtoBERT & 50.23 & 63.67 & 41.49 & 58.09 & 42.34 & 58.46 & 50.14 & 54.61 \\ DemonstrationNER & 57.23 & 65.11 & 46.11 & 58.37 & 46.67 & 59.78 & 52.20 & 56.22 \\ SR-Demonstration & 57.19 & 65.01 & 46.57 & 59.41 & 47.37 & 60.71 & 52.50 & 56.43 \\
**MPSCER-NER** & **58.55** & **67.25** & **47.62** & **60.73** & **48.21** & **62.17** & **53.93** & **57.54** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of the model with different datasets: \(F1\)-score.  
superior NER accuracy of the MPSCER-NER model.The multi-class label prompt selection method mentioned in this study can effectively improve the accuracy rate of positive sample recognition; the core entity replacement method can effectively improve the recall rate. These results are promising for the recognition of proprietary named entities. However, the methods cannot achieve the expected effect in the face of a zero-sample scenario. Methods for avoiding this problem must be researched further.