## 4 Experiments and Analyses
### Effectiveness Study  
We compare CGR-NER with several classic and recently published NER models introduced in the related work. Among them, SDI-NER, FLAT+BERT, AESINER, PLTE+BERT, LEBERT, KGNER and MW-NER enhance the recognition performance of the NER model by introducing a lexicon, syntax knowledge and a knowledge graph; MECT, StyleBERT, GlyNN, Glyce, MFE-NER and ChineseBERT enhance the recognition performance of the NER model by fusing the unique language features of Chinese characters, such as radical, wu-bi, the structural components of Chinese characters and pinyin. The experimental results are shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Weibo** & **OntoNotes 4** & **\#Parameter** \\ \hline Baseline & 14.88 s & 130.90 s & 426.27 M \\ CGR-NER & 19.57 s & 153.57 s & 631.81 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: The total number of parameters and the average time per epoch during training.  
Figure 4: The variation tendency of evaluation metrics and loss value on the development set with the training epoch. (**a**) Weibo dataset; (**b**) OntoNotes 4 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** \\ \hline MECT [29] & - & - & 82.57 \\ MW-NER [27] & - & - & 81.51 \\ LEBERT [26] & - & - & 82.08 \\ KGNER [25] & - & - & 82.10 \\ FLAT+BERT [22] & - & - & 81.82 \\ AESINER [23] & - & - & 81.18 \\ StyleBERT [31] & - & - & 79.10 \\ PLTE+BERT [24] & 79.62 & 81.82 & 80.60 \\ SDI-NER [21] & 82.14 & 78.39 & 80.22 \\ Glyce [32] & 81.87 & 81.40 & 81.63 \\ ChineseBERT [34] & 80.03 & 83.33 & 81.65 \\ BERT-BiLSTM-CRF & 80.51 & 82.23 & 81.34 \\ CGR-NER(ours) & 82.16 & 83.79 & **82.97** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on OntoNotes 4 dataset.  
For the OntoNotes 4 dataset, the precision, recall and F1 score of our model surpass the baseline (i.e., BERT-BiLSTM-CRF) by 1.65%, 1.56% and 1.63%, respectively, indicating that the semantic information contained in the Chinese character glyphs can significantly improve the recognition performance of the model. For the second-best model, MECT, which fuses the lexicon and the structural information of Chinese characters, our model surpasses it by 0.4% for the F1 score. In addition, compared with Glyce, which also utilizes a CNN to extract semantic information from the visual features of glyphs, our model significantly improves by 1.34% for the F1 score. We attribute this improvement to the fact that CGR-NER fully utilizes the potential word formation knowledge between adjacent glyphs and the interaction information between the character representation and glyph representation.  
For the Weibo dataset, the precision, recall and F1 score of our model surpass the baseline by 0.37%, 3.97% and 2.3%, respectively. KGNER and LEBERT achieved the first and second highest F1 scores, respectively. We speculate that this is because the corpus of the Weibo dataset is collected from social media, and the corpus is messy and noisy, which makes the NER task more challenging. Meanwhile, KGNER and LEBERT supplement additional semantic information for the NER model with the help of a knowledge graph and lexicon, respectively, so as to have better performance in the Weibo dataset. It should be noted that in some special fields, such as industrial robot entity recognition, the performance of these models may be limited due to the lack of corresponding knowledge graphs or lexicons. However, CGR-NER still achieved the third highest F1 score without the help of external knowledge, which proves that our model has a strong entity recognition ability. At the same time, CGR-NER does not rely on external knowledge, so it can be applied to a wider range of scenarios.