融合多级语义的中文医疗短文本分类模型OA

To address the issues of insufficient extraction of key semantic information and decreased ro-bustness in medical short text classification,a text classification model that incorporated multi-level se-mantic information was proposed.Firstly,preliminary semantic features of the text were captured by using a pretrained model.Secondly,critical semantic information was extracted through a capsule network,en-suring that the model could effectively learn the core semantics of short texts.Attention pooling tech-niques were applied to focus on document-level information,thereby enhancing the recognition and un-derstanding of medical terminology and concepts.Finally,an adversarial training strategy was introduced to improve the stability and accuracy of the model when faced with ambiguous expressions or perturbed in-puts.The effectiveness of the model was validated on three medical text classification datasets,CHIP-CTC,KUAKE_QIC and VSQ.The results showed that compared to the existing models,the F1 values of the proposed model increased on the three datasets,significantly enhancing the classification performance of Chinese medical short texts.