# Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition
## 4 Experiments
### Experimental Results and Analysis
#### 4.4.5 Efficiency Analysis  
To evaluate the computational efficiency of MMIEE-BERT, we performed an inference experiment comparing the proposed method and the baselines. Specifically, we computed the inference time of each model on the four datasets. Then, we computed the relative inference speed, and the results are reported in Table 13. From the table, we can observe that when decoding with the same batch size (=1), the MMIEE-BERT method was more efficient than Lattice, LSTM, BERT-FLAT, and BERT MECT, performing up to 2.13 times faster than Lattice LSTM. Moreover, compared with BiLSTM-CRF, the inference speed of the BERT-based models was relative slow, because the deep layers limited the inference speed. Finally the MMIEE-BERT method was still faster than the other BERT-based structures. In summary, the proposed MMIEE-BERT model showed advantages over the other methods in terms of inference efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Our Dataset** & **Resume** & **Yidu-S4K** & **MSRA** \\ \hline BiLSTM-CRF & 9.11 & 9.18 & 8.93 & 9.27 \\ Lattice LSTM & 1.00 & 1.00 & 1.00 & 1.00 \\ BERT-BiLSTM & 2.74 & 2.26 & 2.37 & 2.82 \\ BERT-FLAT & 1.94 & 1.93 & 1.85 & 1.83 \\ BERT-MECT & 1.85 & 1.81 & 1.62 & 1.65 \\ LE-BERT & 2.10 & 2.16 & 1.89 & 2.03 \\ Proposed & 2.13 & 2.08 & 1.97 & 2.10 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Relative inference speed (the higher the better) of the proposed model and baselines on different datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding Layer** & **Encoding Layer** & **Layer Number** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline BERT-Base & BiGRU & 1 & 91.10 & 86.83 & 88.91 \\  & Transformer & 1 & 90.36 & 86.98 & 88.64 \\ BERT-Base & BiLSTM & 1 & 90.87 & 87.01 & 88.90 \\  & BiGRU & 2 & 91.09 & 87.38 & 89.20 \\  & Transformer & 2 & 90.86 & 87.29 & 89.04 \\  & BiLSTM & 2 & 91.03 & 87.09 & 89.02 \\ \hline BERT-L-M & BiGRU & 1 & 90.88 & 88.18 & 89.51 \\  & Transformer & 1 & 91.15 & 88.40 & 89.76 \\ BERT-L-M & BiLSTM & 1 & 90.87 & 88.26 & 89.54 \\  & BiGRU & 2 & 91.09 & 88.49 & 89.78 \\  & Transformer & 2 & 91.56 & 89.13 & 90.33 \\  & BiLSTM & 2 & 91.69 & 89.17 & 90.41 \\ \hline BERT-R-M & BiGRU & 1 & 91.02 & 88.29 & 89.64 \\  & Transformer & 1 & 91.07 & 88.66 & 89.85 \\ BERT-R-M & BiLSTM & 1 & 90.94 & 88.70 & 89.80 \\  & BiGRU & 2 & 91.00 & 88.55 & 89.76 \\  & Transformer & 2 & 91.06 & 88.73 & 89.88 \\  & BiLSTM & 2 & 91.23 & 88.84 & 90.02 \\ \hline BERT-LR-M & BiGRU & 1 & 91.02 & 88.44 & 89.71 \\  & Transformer & 1 & 91.49 & 89.23 & 90.34 \\ BERT-LR-M & BiLSTM & 1 & 91.24 & 88.93 & 90.07 \\  & BiGRU & 2 & 91.44 & 89.10 & 90.25 \\  & Transformer & 2 & 91.95 & 89.43 & 90.67 \\  & BiLSTM & 2 & 91.84 & 89.39 & 90.60 \\ \hline  & BiGRU & 1 & 92.01 & 89.46 & 90.72 \\  & Transformer & 1 & 92.02 & 89.83 & 90.91 \\ MMIEE-BERT & BiLSTM & 1 & 92.47 & 89.88 & 91.16 \\  & BiGRU & 2 & 91.69 & 89.59 & 90.63 \\  & Transformer & 2 & 92.23 & 89.92 & 91.06 \\  & BiLSTM & 2 & **92.90** & **90.27** & **91.57** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Statistical results of encoding layer and embedding layer comparison experiments.