## 5 Conclusions  
In order to make full use of the rich semantic information contained in Chinese character glyphs and improve the recognition performance of Chinese NER, this paper proposes a novel Chinese NER model named CGR-NER. In CGR-NER, a hybrid neural network  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**OntoNotes 4**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{BERT-BiLSTM-CRF} & -w/o OOV & 69.39 & 69.69 & 69.48 & 81.71 & 82.85 & 82.26 \\  & -w OOV & 74.92 & 47.14 & 57.79 & 72.36 & 76.75 & 74.53 \\ \hline \multirow{2}{*}{CGR-NER} & -w/o OOV & 70.70 & 72.27 & 71.43 & 82.33 & 83.71 & 83.01 \\  & -w OOV & 73.28 & 58.57 & 61.11 & 76.50 & 80.48 & 78.43 \\ \hline \hline \end{tabular}
\end{table}
Table 11: NER model performance on the test subset with and without out-of-vocabulary characters.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{\
\begin{tabular}{l} Sentence 1 \\ (Isn’t [PERSON]? Oh, that little red face) \\ \end{tabular} } \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Gold label \\ \end{tabular} } & \#(B-PER.NAAM)\#(E-PER.NAAM)\#(C)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O) \\ \hline \multirow{2}{*}{BERT-BiLSTM-CRF} & \#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O)\#(O) \\ \hline \multirow{2}{*}{CGR-NER} & \#(B-PER.NAAM)\#(E-PER.NAAM)\#(C)\#(O)\#  
CBHNN is applied to capture the semantic information contained in Chinese character glyphs, the potential word formation knowledge between adjacent glyphs and the unique contextual semantics and global dependencies of glyph sequences. In addition, a fusion method with a crossmodal attention and gate mechanism is proposed to dynamically fuse the contextual representation from text and the glyph representation from images, so that the model can learn the complementary information between different modal representations. We conducted sufficient experiments on two mainstream Chinese NER datasets. The experimental results showed that CGR-NER achieved 70.70% and 82.97% F1 scores on the Weibo dataset and OntoNotes 4 dataset, which were increased by 2.3% and 1.63% compared with the baseline, respectively. At the same time, we conducted multiple groups of ablation experiments, proving that CGR-NER can still maintain good recognition performance in the condition of insufficient training data and out-of-vocabulary characters.  
In addition, our model also has some limitations. Since CGR-NER carries out Chinese NER at the character level, it may lack the word-level features. In the future, we will consider how to more effectively integrate characters, glyphs and word-level information into the Chinese NER model, and continue to study the entity recognition effect of our model in specific domains to verify its robustness and generalization ability.  
Conceptualization, R.G. and T.W.; software, R.G.; validation, R.G. and J.D.; writing--original draft preparation, R.G. and J.D.; writing--review and editing, T.W. and L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Program of NSFC-Guangdong Joint Funds (U2001201), Industrial core and key technology plan of ZhuHai City (ZH22044702190034 HJL), the National Natural Science Foundation of China (Grant No. U1801263) and the National Key R&D Program of China (Grant No. 2019 YFB1705500). Our work is also supported by Guangdong Provincial Key Laboratory of Cyber-Physical System (2020B1212060069).  
Not applicable.  
The authors declare no conflict of interest.