# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Method** & **Performance** & **Disadvantages** \\ \hline [PERSON] et al. [10] & Regular expression-based matching rules & Precision (81.47\%) & Their method involves complex matching rules and high manual costs and has a low generalization capability. \\ \hline [PERSON] et al. [11] & A series of rules with different properties & Precision (91.40\%) & Their method involves complex matching rules and high manual costs and has a low generalization capability. \\ \hline [PERSON] [12] & CRF & F1 score (69.50\%) & Their method still requires manual effort for feature engineering before model training, and its generalization capability is insufficient. \\ \hline [PERSON] et al. [13] & HMM (slightly modified version) & F1 score (93.00\%) & Their method still requires manual effort for feature engineering before model training, and its generalization capability is insufficient. \\ \hline [PERSON] et al. [14] & ID-CNN-CRF & F1 score (90.54\%) & Their method considers only the local features of the input data. \\ \hline [PERSON] et al. [15] & BI-LSTM-CRF & F1 score (90.10\%) & Their method considers only the global features of the input data. \\ \hline [PERSON] et al. [18] & S-LSTM & F1 score (90.33\%) & Their method considers only the global features of the input data. \\ \hline [PERSON] and [PERSON] [19] & CNN-BLSTM-CRF & F1 score (89.09\%) & Although their method considers both the local and global features of the input data, the local features are processed sequentially as a preliminary step for global feature extraction, without fully accounting for the highly nonlinear relationships between the local and global feature perspectives. \\ \hline [PERSON] and [PERSON] [6] & Fusion multi-feature-CNN-BiLSTM-CRF & F1 score (78.07\%) & F1 score (78.07\%) & Although their method considers both the local and global features of the input data, the local features are processed sequentially as a preliminary step for global feature extraction, without fully accounting for the highly nonlinear relationships between the local and global feature perspectives. \\ \hline [PERSON] et al. [20] & CNN-BLSTM-CRF+W2V+ELMo & F1 score (83.75\%) & Although their method considers both the local and global features of the input data, the local features are processed sequentially as a preliminary step for global feature extraction, without fully accounting for the highly nonlinear relationships between the local and global feature perspectives. \\ \hline [PERSON] et al. [21] & BiLSTM-CRF-Load & F1 score (92.59\%) & Their method considers only the global features of the input data. \\ \hline [PERSON] et al. [22] & GCDoc & F1 score (92.22\%) & Their method considers only the global features of the input data. \\ \hline [PERSON] et al. [23] & CEPTNER & F1 score (69.52\%) & Their method does not consider the local and global features of the input data; it only performs basic encoding and decoding on the input data. \\ \hline [PERSON] et al. [24] & UACL & F1 score (76.35\%) & Their method does not consider the local and global features of the input data; it only performs basic encoding and decoding on the input data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of existing named entity recognition methods.