# SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics
## 4 Experiments  
### Datasets  
* **Target Domain Datasets**  
To validate the effectiveness of our model, we used three benchmark Chinese datasets and a self-built dataset as target domain datasets:  
**Ontonotes 4.0**[36]: This dataset comprises corpora from the news and broadcast domains, covering four entity types.  
**MSRA**[37]: This dataset comprises corpora from the news domain and includes three entity types.  
**Resume**[11]: This dataset consists of abstracts from resumes of senior managers in publicly listed companies. It encompasses eight entity types.  
**RISK**: This dataset was created by us and focuses on food safety risk-related corpora. We hierarchically categorized the dataset into 5 coarse-grained and 20 fine-grained entity types, as shown in Figure 8. The entity count for each fine-grained category is illustrated in Figure 9. The distribution of entity categories is not uniform, making RISK challenging at the fine-grained level.  
The detailed statistics of the target domain datasets are presented in Table 2. To ensure the comparability of experimental results, we followed the sampling approach of PCBERT [10] to simulate a few-shot scenario (see Section 4.4 for details). Compared to the N-way K-shot setting, we believe that this sampling approach is more representative of realistic few-shot scenarios. We further divided the datasets into scenarios with even fewer examples and tested our model, demonstrating its effectiveness even in scenarios with fewer data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Entity Label** & **Full Label Name** \\ \hline  & LOC-PROV & \(\#\) (province) \\  & LOC-PREF & \(\#\), \(\mathbb{K}\) (city, district) \\  & LOC-COUNT & \(\#\) (country) \\  & FOOD-VEG & \(\#\) (vegetable) \\  & FOOD-FRUIT & \(\#\) (fruit) \\  & FOOD-MEAT & \(\#\) (meat) \\  & FOOD-GRAIN & \(\#\) (foodstuff) \\  & FOOD-DAIRY & \(\#\) (dairy products and beverages) \\  & CO-PROC & \(\#\) (processing and production methods) \\  & CO-PROD & \(\#\), \(\mathbb{K}\) (company, factory) \\ RISK & CO-TRAD & \(\#\) (sales store) \\  & CO-CATE & \(\#\) (hotel) \\  & CO-MATE & \(\#\) (supermarket) \\  & ORG-REGU & \(\#\) (supervisory authority) \\  & ORG-ADMI & \(\#\) (management department) \\  & ORG-LEGA & \(\#\) (legal agency) \\  & ORG-SOCI & \(\#\) (social organization) \\  & RISK-HIGH & \(\#\) (virus) \\  & RISK-MID & \(\#\) (chemicals, bacteria) \\  & RISK-LOW & \(\#\) (additive) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* **Source Domain Dataset** We adopted the source domain dataset used in the PCBERT to ensure experimental consistency and reliability. This high-resource dataset is a combination of multiple  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Dev** & **Test** & **Entity Types** \\ \hline Ontonotes & 15.7 k & 4.3 k & 4.3 k & 4 \\ MSRA & 41.7 k & 4.6 k & 4.3 k & 3 \\ Resume & 3.8 k & 0.46 k & 0.48 k & 8 \\ RISK & 1.2 k & 0.26 k & 0.26 k & 5 (20) 1  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed statistical table for the target domain dataset.  
Figure 8: Hierarchy chart of the RISK dataset.  
Figure 9: Statistical chart of the number of various entity types in the RISK dataset.  
datasets, including CLUENE [38], CNERTA [39], RenMinRiBao [40], and datasets from unknown sources.