# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 4 Experiments and Results Analysis
### Experimental Results and Analysis  
We trained our model using named entity recognition and intent classification training datasets, and tested the performance of named entity recognition and intent classification \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Quantity** & **Maximum Length** & **Minimum Length** \\ \hline clinical manifestation (disease symptoms) & 1313 & 217 & 5 \\ related diseases & 242 & 139 & 8 \\ treatment methods & 2101 & 185 & 5 \\ departments & 26 & 60 & 6 \\ infectivity & 43 & 111 & 5 \\ cure rate & 144 & 135 & 6 \\ drug taboos & 235 & 102 & 7 \\ laboratory/physical examination programs & 80 & 107 & 7 \\ treatment time & 41 & 119 & 9 \\ others & 2332 & 221 & 5 \\ total & 8105 & 221 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._tasks using F1 values, recall rates, and accuracy rates to evaluate model performance in this study. The model proposed in this paper was validated in the test set. For the entity recognition task, the accuracy of the model reached 0.8607 in the test set, and for the intention recognition task, the accuracy of the model reached 0.8925. In order to verify the superiority of the model, the constructed model was compared with BERT, BERTCRF, Bert-BiLSTM and Bert-BiLSTM-CRF models for the entity recognition task, and the results are shown in Table 6:  
As can be seen from Table 6, the method proposed in this paper has higher accuracy and F1 value, and maintains a high recall rate of 0.8035, while the accuracy reaches 0.8607, which shows its superiority. This shows that the method proposed in this paper can improve the performance of entity recognition. From the comparison, the main conclusions are as follows:  
(1) The output of BERT is often the result of word-by-word classification. Adding a CRF layer can optimize the output and make the dependency between tag sequences more reasonable, so as to improve the overall consistency of sequence tagging. Through comparison, it can be found that CRF can effectively capture more relevant information, reduce the missed detection rate, and improve the overall F1 score. (2) Compared with using BERT or BiLSTM alone, it has the advantage of capturing long-distance dependencies and context information, and is more able to identify complex sequence dependencies. However, compared with simple BERT, the performance improvement of the BERT-BiLSTM model is limited. With the combination of BiLSTM and CRF layers, the model can make use of both the contextual feature extraction ability of BiLSTM and the tag dependency modeling ability of CRF. BiLSTM handles long-distance dependencies and CRF optimizes the dependencies of output tags. The combination of the two makes the model significantly improve in entity recognition tasks. In particular, both precision and recall are improved, which shows that the model has achieved good results in reducing missed detection and false detection. (3) By adding BiGRU and BiLSTM layers to the output of BERT, the model can make full use of two different cyclic neural network (RNN) structures to process sequence data. BiGRU is relatively simple and can quickly capture short-term dependencies, while BiLSTM is better at capturing long-term dependencies. This dual RNN architecture can effectively balance the capture ability of short-term and long-term dependencies and make the model more robust when dealing with serial data with complex dependencies. This characteristic is particularly evident in the identification of long-distance dependent entities. From the experimental results, this model achieves a good balance between accuracy and recall rate. Compared with other models, this model performs well in capturing the correct entities and covering as many entities as possible. This balance makes the model more practical in real-world application, as it can not only reduce false detection (improve accuracy), but also reduce missed detection (improve recall rate), so as to provide users with more reliable entity recognition results.