融合角色心理画像的心理健康文本匹配模型  

Mental Health Text Matching Model Integrating Characters’Mental Portrait

作　　者：赵芸 刘德喜[1,3] 万常选[1,3] 刘喜平[1,3] 廖国琼[1,3] Zhao Yun;Liu Dexi;Wan Changxuan;Liu Xiping;Liao Guoqiong(School of Information Management,Jiangxi University of Finance and Economics,Nanchang 330013;School of Mathematics and Computer Science,Jiangxi Science and Technology Normal University,Nanchang 330038;Jiangxi Key Laboratory of Data and Knowledge Engineering(Jiangxi University of Finance and Economics),Nanchang 330013)

机构地区：[1]江西财经大学信息管理学院,南昌330013 [2]江西科技师范大学数学与计算机科学学院,南昌330038 [3]数据与知识工程江西省高校重点实验室(江西财经大学),南昌330013

出　　处：《计算机研究与发展》2024年第7期1812-1824,共13页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62272206,62272205,62076112,62262027);江西省自然科学基金项目(20212ACB202002);江西省主要学科学术和技术带头人培养计划领军人才项目(20213BCJL22041);江西省教育厅科学技术研究项目(GJJ2200501,GJJ2201335);江西科技师范大学博士科研启动基金项目(2022BSQD36)。

摘　　要：全球心理健康问题形势严峻,由于心理健康服务的从业人员不足,遭受心理健康困扰的人并不总是能获得专业的心理健康服务.检索式心理健康社区自动问答可以快速地为需要心理健康服务的人提供相应的信息自助服务.与传统检索式社区问答中的文本匹配不同,在匹配支持帖和求助帖时,需要考虑2种不同层面的匹配准则:语义层面和心理层面.为了解决该问题,提出融合角色心理画像的2阶段文本匹配模型(two-stage text matching model integrating characters’mental portrait,T2CMP),该模型引入心理特征用于构建角色心理画像,从而辅助模型理解文本心理层面的内容和匹配关系.同时为了提升检索效率以及减少大量负样例带来的噪声问题,将文本匹配任务拆分为2阶段的序列型子任务.首先针对每条求助帖,使用基于语义的筛选模型甄别出候选支持帖;然后依据用户的角色心理画像,使用多层注意力机制将其与语义信息有效融合,提高模型的总体效果.在MHCQA数据集上的实验结果显示,T2CMP比现有优秀算法拥有更高的F1值.The global situation regarding mental health problems is a matter of serious concern.Unfortunately,in many countries,the shortage of mental health practitioners often leaves individuals with mental health problems without access to professional services.The retrieval-based community question answering for mental health can provide self-service information for those who are suffering from mental health problems.There are two different matching criteria to consider when matching question post and support response in this task,namely semantic matching and mental health matching.To address the issue of excessive non-ideal support responses in a large pool of candidate support responses,we propose a two-stage text matching model that integrates characters’mental portraits using a multi-layer attention mechanism.This method divides our task into two sequential subtasks:a retrieval task and a matching task,respectively.In the first stage,a retrieval sub-method is used to retrieve a set of candidate support responses with a certain semantic similarity for each question post.In the second stage,the matching sub-method performs matching judgments based on the results generated in the previous stage.This sub-method applies a multilayer attention mechanism to effectively integrate characters’mental portraits and semantics.This process can modify the results of simple semantic matching.Experimental results show that T2CMP proposed in this paper is superior to state-of-the-art methods.

关 键 词：文本匹配 2阶段模型 角色心理健康画像 多层注意力机制 心理健康信息自助服务 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]