# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 2 Related Work
### Semantic Analysis
[9] proposed a joint model based on BERT, which solves the label dislocation problem caused by subword segmentation by introducing subword attention adapters and intent attention adapters, and achieves significant improvement in intention classification and slot filling tasks. Although BERT-based models perform well in many tasks, their ability to generalize to low-resource languages and domains is still limited. In addition, many models do not perform well when dealing with long text or complex context dependencies, resulting in excessive computational overhead. In order to further improve the effect, researchers combined various models. [PERSON] et al. [10] proposed an improved GL-GIN model, which realized bidirectional information flow by introducing slots into the unidirectional attention layer and BERT coding layer of intent, and significantly improved the performance of the multi-intent recognition model. In addition, the researchers found that the combination of BERT, BiGRU/BiLSTM, and CRF to identify the intention information and entity information in the question can improve the accuracy and task performance of the model. [PERSON] et al. [11] proposed a question-and-answer model based on BERT-BiGRU-CRF, and the final F1 value of the model was 76.39%. [PERSON] et al. [12] proposed a method based on BERT and the BERT-BiLSTM-CRF model to identify intention information and entity information in questions, which makes the returned answers more accurate. [PERSON] et al. [13] proposed a Chinese medical entity recognition model based on BERT-BiGRU-CRF, which is improved compared with other models by combining the word vector representation of BERT, context feature extraction of BiGRU, and tag optimization of CRF. Although the current methods have improved the accuracy of semantic understanding of medical texts to a certain extent, there is still room for improvement in the processing of long and complex sentences in complex natural languages. In addition, relying on a single model (such as LSTM or GRU) often faces the problem of insufficient understanding of context information and a limited ability to extract features. In this paper, a multi-task learning method based on BiGRU-BiLSTM is proposed to enhance the comprehensive understanding and parsing ability of medical texts. Table 1 summarizes the current research on semantic parsing, which provides a reference for the follow-up research on entity recognition and intention recognition combined with the multi-task learning model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Methods and Techniques** & **Research Findings** & **Prospect** \\ \hline \multirow{4}{*}{Reference [3]} & Medical text classification method based on concept map based on SVM, using domain specific matrix and graph kernel classification & High-performance results were achieved in English biomedical literature and clinical report classification & Extend the method to other languages and different medical fields to improve the generalization ability \\  & Four shallow machine learning models (naive Bayesian, logical regression, SVM, k-NN) and integrated classifier chain (ECCCLR, ECCSVM) & It performs well in the multi-tag classification of Russian medical texts, which improves the accuracy and recognition rate of classification & It is planned to use the NER model to extract entity information to further improve the performance of the model \\  & A hybrid model based on CNN and GRU to capture the semantic relationship between questions and answers & The performance of the model is improved, but there are some problems such as paying attention to word matching and interlexical association & Solve the problem of insufficient connection between words, and further improve the semantic comprehension ability of long text \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of current research status of semantic parsing.