# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 4 Experiments and Results Analysis
### Ablation Experiment  
In order to test the effect of the proposed method on performance and explore the effect on contextual information and complex text, ablation experiments on the above models were carried out on CBLUE and CMID datasets. The experimental results are shown in Table 8.  
Among them, Modules 1 and 3 are the single task test results of entity recognition and intention recognition, respectively. For entity recognition and intention recognition tasks, F1 values decrease by 1.8% and 1.2%, respectively, compared with the model proposed in this paper, which verifies that multi-task learning can effectively capture the correlation between different tasks. The model's ability to understand specific tasks is improved. It can be seen from the ablation experiment that in the entity recognition task, the auxiliary task of intention recognition enables the model to better understand the semantic relationship in \begin{table}
\begin{tabular}{c c c c} \hline  & **Precision** & **Recall** & **F1** \\ \hline CNN & 0.8315 & 0.7921 & 0.8113 \\ BILSTM & 0.8377 & 0.8086 & 0.8229 \\ BERT & 0.8582 & 0.8391 & 0.8485 \\ TextCNN & 0.8625 & 0.8511 & 0.8567 \\ TextRNN & 0.8634 & 0.8602 & 0.8618 \\ Ours & 0.8925 & 0.8832 & 0.8878 \\ \hline \end{tabular}
\end{table} 
Table 7: Intention recognition and comparison of experimental results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Precision** & **Recall** & **F1** \\ \hline Module1 & 0.8368 & 0.8017 & 0.8189 \\ Module2 & 0.8319 & 0.8203 & 0.8261 \\ Ours-N & 0.8607 & 0.8135 & 0.8364 \\ \hline Module3 & 0.8838 & 0.8673 & 0.8754 \\ Module4 & 0.8628 & 0.8503 & 0.8565 \\ Ours-I & 0.8925 & 0.8832 & 0.8878 \\ \hline \end{tabular}
\end{table} 
Table 8: Ablation results. the context, thus improving the accuracy of entity recognition. In the intention recognition task, the additional features provided by entity recognition enhance the model's understanding of the user's input intention, and further improve the accuracy of the intention recognition. Ablation results also show that multi-task learning can make better use of training data, especially in the case of relatively limited data. By training multiple tasks at the same time, the model can more fully learn the common features between different tasks, thereby improving the overall performance. However, it has to be admitted that although multi-task learning has the potential to improve the performance of the model, negative transfer is a potential problem that needs to be paid attention to. Future research should focus on how to effectively alleviate this phenomenon and find ways to optimize task combination and the sharing mechanism to ensure that the impact of negative migration on model performance is reduced while maximizing task synergy. Modules 2 and 4 are the experimental results of entity recognition and intention recognition after the BiGRU layer is removed from the sharing layer, respectively. For entity recognition and intention recognition tasks, F1 values are reduced by 1% and 3.1%, respectively, but the recall rate of entity recognition tasks is increased by 0.7%, which verifies the ability of two-way information flow with BiGRU. The ability to capture forward and backward context dependencies simultaneously plays an important role in deep secondary feature extraction in the framework of multi-task learning. Through the ablation experiment, we can see that after removing the BiGRU layer, although the recall rate in the entity recognition task is slightly improved, the overall F1 value decreases; in particular, in the intention recognition task, the F1 value decreases significantly by 3.1%. The BiGRU layer can capture the long-term dependency information in the sequence data; in particular, in the intention recognition task, it helps the model to understand the global information of the context more accurately and improve the ability to identify complex intentions.