# SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics
## 4 Experiments
### Experimental Results  
Our model was thoroughly evaluated on four sampled Chinese NER datasets. Specifically, we sampled K = 250, K = 500, K = 1000, and K = 1350 examples from the Ontonotes, MSKA, and Resume datasets, respectively, as training sets to cover different levels of data scarcity and comprehensively evaluate the training effectiveness and robustness of the model. The standard F1 score is used as the evaluation metric to measure the final performance of the model. Table 3 shows the experimental results of our proposed model and the baseline models on three benchmark NER datasets. The results indicate that, except for the 1350-shot setting in the resume dataset, our method outperforms the state-of-the-art models by 0.2-6.57 percent, demonstrating the excellent performance of our method in few-shot settings. Table 4 presents the experimental results of our proposed model on the RISK dataset. Compared to coarse-grained classification, the model shows a maximum decrease of 3.64-6.11 percent in F1 score under fine-grained classification. This indicates that the dataset is more challenging in fine-grained classification. In addition, to further validate the feasibility of our model in a low-resource setting, we also sampled data under K = 10, K = 20, and K = 50 settings and evaluated the effectiveness of our model with even fewer examples. The results are shown in Table 5. The experimental results demonstrate that our model can still achieve good results, even with as few as a few dozen samples. We analyze this phenomenon in Section 5.3. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **K-Shot** & **Coarse-Grained** & **Fine-Grained** \\ \hline \multirow{4}{*}{RISK} & K = 250 & 69.67 & 63.97 \\  & K = 500 & 71.73 & 65.62 \\ \cline{1-1}  & K = 1000 & 72.70 & 68.06 \\ \cline{1-1}  & Full 1  & 72.62 & 68.98 \\ \hline \end{tabular}
\end{table} 
Table 4: The results on the RISK dataset, showing the F1 scores for different sampling sizes in both coarse-grained and fine-grained categories. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Method** & **K = 250** & **K = 500** & **K = 1000** & **K = 1350** \\ \hline \multirow{6}{*}{Ontonotes} & BERT & 63.85 & 69.50 & 71.33 & 72.42 \\  & BERT-LC & 65.69 & 73.54 & 74.97 & 77.19 \\  & Lattice LSTM & 39.71 & 45.46 & 54.54 & 57.48 \\  & FLAT & 49.01 & 46.35 & 49.34 & 57.44 \\  & LEBERT & 69.48 & 69.01 & 73.78 & 74.84 \\  & LEBERT-LC & 70.26 & 69.89 & 73.83 & 76.01 \\  & PCBERT & 74.42 & 75.62 & 78.33 & 81.52 \\  & **SLNER (ours)** & **77.66** & **80.46** & **81.53** & **82.23** \\ \hline \multirow{6}{*}{MSRA} & BERT & 68.44 & 72.28 & 81.21 & 82.28 \\  & BERT-LC & 79.01 & 83.13 & 87.84 & 89.32 \\  & Lattice LSTM & 54.69 & 63.61 & 74.27 & 76.31 \\  & FLAT & 59.62 & 70.20 & 80.79 & 64.95 \\  & LEBERT & 79.11 & 85.18 & 87.77 & 89.35 \\  & LEBERT-LC & 80.92 & 86.09 & 88.11 & 88.70 \\  & PCBERT & 81.08 & 85.25 & 87.88 & 89.72 \\  & **SLNER (ours)** & **87.65** & **89.30** & **89.67** & **90.08** \\ \hline \multirow{6}{*}{Resume} & BERT & 53.80 & 62.64 & 69.36 & 70.65 \\  & BERT-LC & 92.26 & 94.66 & 95.16 & **96.41** \\ \cline{1-1}  & Lattice LSTM & 85.63 & 89.60 & 92.01 & 93.13 \\ \cline{1-1}  & FLAT & 84.62 & 90.77 & 92.97 & 87.79 \\ \cline{1-1}  & LEBERT & 89.15 & 92.56 & 94.02 & 95.19 \\ \cline{1-1}  & LEBERT-LC & 91.60 & 93.03 & 95.40 & 95.16 \\ \cline{1-1}  & PCBERT & 93.42 & 94.01 & 94.96 & 95.97 \\ \cline{1-1}  & **SLNER (ours)** & **94.02** & **94.86** & **95.99** & 96.36 \\ \hline \end{tabular}
\end{table} 
Table 3: F1 scores for the four Chinese sampling NER datasets. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **K-Shot** & **Coarse-Grained** & **Fine-Grained** \\ \hline \multirow{4}{*}{RISK} & K = 250 & 69.67 & 63.97 \\  & K = 500 & 71.73 & 65.62 \\ \cline{1-1}  & K = 1000 & 72.70 & 68.06 \\ \cline{1-1}  & Full 1  & 72.62 & 68.98 \\ \hline \end{tabular}
\end{table}