## 4 Experiments and Analyses
### Ablation Study
#### 4.5.3 Effect of CBHNN  
To verify the effectiveness of CBHNN in capturing the semantic information contained in Chinese character glyphs, we used glyph encoders with different structures to generate a glyph representation, and conducted experiments on the Weibo and OntoNotes 4 datasets, as shown in Table 10. Among them, 'character' represents BERT-BiLSTM-CRF, which only has a contextual representation as input. Tianzige is Tianzige-CNN proposed by [PERSON] [32]. Tianzige-BiLSTM represents that BiLSTM is used to capture the hidden state of Tianzige output, which is used as the glyph representation input of the model. CBHNN\({}^{CNN}\) represents the CBHNN with no BiLSTM. Other settings of CGR-NER remained unchanged.  
According to Table 10, the model based only on the contextual representation as input achieves the worst result because it ignores the semantic information contained in the Chinese character glyphs. Compared with Tianzige, the F1 scores of CBHNN\({}^{CNN}\) on Weibo and OntoNotes 4 are improved by 0.6% and 0.34%, respectively, for the reason that the CBHNN\({}^{CNN}\) can not only capture the semantic information in Chinese character glyphs, but also learns the potential word formation knowledge between adjacent glyphs through 3D convolution, thus enriching the semantic information of the representation. After Tianzige and CBHNN\({}^{CNN}\) are combined with BiLSTM, respectively, the performance of the NER model is further improved, indicating that the context semantics and global dependency features in the glyph sequence are helpful for entity recognition. In contrast, CBHNN, which combines 3 DCNN and BiLSTM, achieves the best F1 scores with 70.70% and 82.97% on the Weibo and OntoNotes 4 datasets, respectively. Hence, the experimental results show that CBHNN can effectively learn the semantic information contained in Chinese character glyphs, the potential word formation knowledge between adjacent glyphs and the context semantics and global dependency features of glyph sequences, thus improving the recognition performance of our proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**OntoNotes 4**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline character & 69.86 & 67.20 & 68.40 & 80.51 & 82.23 & 81.34 \\ Tianzige & 70.23 & 69.57 & 69.89 & 81.31 & 82.99 & 82.14 \\ BiLSTM & 70.40 & 69.83 & 70.11 & 81.75 & 83.44 & 82.58 \\ CBHNN\({}^{CNN}\) & 70.22 & 70.66 & 70.49 & 81.76 & 83.22 & 82.48 \\ \hline CBHNN & 70.23 & 71.17 & 70.70 & 82.16 & 83.79 & 82.97 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Result of CGR-NER with different glyph encoders.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**OntoNotes 4**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline CGR-NER & 70.23 & 71.17 & 70.70 & 82.16 & 83.79 & 82.97 \\ \hline \(-\)w/o Glyph & 69.86 & 67.20 & 68.40 & 80.51 & 82.23 & 81.34 \\  & (\(-\)0.37) & (\(-\)3.97) & (\(-\)2.3) & (\(-\)1.65) & (-1.56) & (\(-\)1.63) \\ \hline \(-\)w/o GM & 70.38 & 69.83 & 70.10 & 81.82 & 83.70 & 82.75 \\  & (0.15) & (\(-\)1.34) & (\(-\)0.6) & (\(-\)0.34) & (\(-\)0.09) & (-0.22) \\ \hline \(-\)w/o CMTE & 70.27 & 70.05 & 70.16 & 81.90 & 83.52 & 82.69 \\  & (0.04) & (\(-\)1.12) & (\(-\)0.54) & (\(-\)0.26) & (\(-\)0.27) & (\(-\)0.28) \\ \hline \(-\)w/o CMTE+GM & 69.96 & 69.76 & 69.85 & 81.96 & 82.89 & 82.42 \\  & (\(-\)0.27) & (\(-\)1.41) & (\(-\)0.85) & (\(-\)0.2) & (\(-\)0.9) & (\(-\)0.55) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation experiment results on Weibo and OntoNotes 4 datasets.