# Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
## 4 Experimental Setup and Results
### Experimental Results
#### 4.2.2 Results of the Methods in Two Different Conditions  
The second experiment was carried out to verify the effectiveness of the model. We selected four groups of models--the BiLSTM-CRF model, the BiGRU-CRF model, the Transformer-BiLSTM-CRF, and the Transformer-BiGRU-CRF model for forest disease named entity recognition proposed in this paper for comparative experiments. Then, we compared the models in two environments: character vector embedding and multi-feature fusion embedding. The experimental results are shown in Tables 5 and 6.  
Results of the different methods for character vector embedding are shown in Table 5. The Transformer-BiGRU-CRF model constructed in this paper was better than other methods, with precision of 90.11%, recall rate of 92.37%, and F1 value of 91.02%, which shows that this method can effectively model the text of forest diseases and has adequate adaptability for the forest disease texts. The precision of the BiLSTM-CRF model was 85.20%, indicating that the BiLSTM network structure can extract the implicit context features, effectively solve the sequence problem, and complete the named entity recognition task  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **P** & **R** & **F1** \\ \hline Char & 90.70 & 89.06 & 89.87 \\ POS & 83.03 & 73.19 & 77.80 \\ Boundary & 88.40 & 82.63 & 85.42 \\ Radical & 91.68 & 90.66 & 91.17 \\ RBP & 93.16 & 92.97 & 93.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model multi-feature embedding results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline BiLSTM-CRF & 85.20 & 86.85 & 86.02 \\ BiGRU-CRF & 87.42 & 86.55 & 86.98 \\ Transformer-BiLSTM-CRF & 88.98 & 88.35 & 88.66 \\ Transformer-BiGRU-CRF & 90.70 & 89.06 & 89.87 \\ RBP-Transformer-BiGRU-CRF & 93.16 & 92.97 & 93.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the different methods for character vector embedding.  
in forest disease text data sets. The precision of the BiGRU-CRF model was 2% higher than that of the BiLSTM-CRF model, which shows that the simplified gating structure of the BiGRU network structure has a better generalization ability and better performance in forest disease text data sets. The experimental results for the Transformer-BiLSTM-CRF model showed that the accuracy and F1-values were slightly improved compared with those of the BiLSTM-CRF model. The introduction of a transformer decoding layer model can, therefore, enhance the feature extraction ability of the model and improve the recognition efficiency.  
Results of the various models for multi-feature embedding are shown in Table 6. The comparative experiment was conducted for each model after the multi-feature embedding input. The precision and F1-values for each algorithm based on multi-feature embedding were improved. The RBP-Transformer-BiGRU-CRF model proposed in this paper has the precision of 93.16%, the recall rate of 92.97%, and the F1-value of 93.07%. Under the condition of multi-feature embedding, the model significantly outperforms other models, and obtains favorable experimental results.