嵌入意图识别的医疗健康问答文本语义分类模型  

Semantic Classification Model for Healthcare Q&A Texts with Embedded Intent Recognition

作　　者：谌文佳 杨琳 李金林[2] Chen Wenjia;Yang Lin;Li Jinlin(College of Management Science and Engineering,Beijing Information Science&Technology University,Beijing 100192,China;School of Management,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京信息科技大学管理科学与工程学院,北京100192 [2]北京理工大学管理学院,北京100081

出　　处：《数据分析与知识发现》2025年第2期26-38,共13页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71972012);北京信息科技大学基金项目(项目编号:2023XJJ21)的研究成果之一。

摘　　要：【目的】基于意图识别构建更准确的语义相似度分类模型,为中文医疗健康查询服务提供更精准的答案匹配结果。【方法】融合BERT和卷积神经网络(CNN)构建意图识别模型,然后将其作为模型嵌入层构建嵌入意图识别的孪生BERT(ITBERT)语义分类模型。【结果】在CHIP-STS数据集上,相较于BERT和TextCNN单一模型,融合模型在意图识别的Top-1结果准确率分别提高了1.5和8.2个百分点,达到了73.6%;Top-3结果准确率分别提高了3.2和7.6个百分点,达到了91.2%,证明了融合模型对于意图识别效果的提升。对于语义相似度分类结果,相较于基准模型,ITBERT模型AUC值提高了0.015~0.087,证明了意图知识的嵌入对医疗语义相似度分类效果的提升。【局限】人工标注的意图信息存在一定的偏差,可能会影响语义相似度的分类结果。【结论】融合模型可以改善医疗健康查询服务中的意图识别效果,嵌入识别的意图知识可以提高语义相似度分类模型的准确率,有利于提供更准确的医疗健康自动问答服务。[Objective]This study builds a more accurate semantic similarity classification model based on intent recognition to provide precise answer-matching results for Chinese medical and health Q&A services.[Methods]We integrated BERT and Convolutional Neural Networks(CNN)to construct an intent recognition model,which is used as an embedding layer to develop an intent-recognizing twin BERT(ITBERT)semantic classification model.[Results]On the CHIP-STS dataset,compared to single BERT and TextCNN models,the integrated model improved the Top-1 accuracy of intent recognition by 8.2%and 1.5%,reaching 73.6%.The Top-3 accuracy improved by 7.6%and 3.2%,reaching 91.2%,demonstrating the new model's enhanced intent recognition effectiveness.For semantic similarity classification,the ITBERT model improves the AUC value by 0.015 to 0.087 compared to benchmark models,proving that embedding intent knowledge improves the effectiveness of medical semantic similarity classification.[Limitations]Manually annotated intent information may contain biases and affect the classification results of semantic similarity.[Conclusions]The proposed model can improve intent recognition in medical and health Q&A services.Embedding intent knowledge enhances the accuracy of semantic similarity classification models,contributing to more precise automated Q&A services.

关 键 词：意图识别 医疗问答 语义相似度分类 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]