基于上下文知识增强型Transformer网络的抑郁检测  

Depression Detection Based on Contextual Knowledge Enhanced Transformer Network

作　　者：张亚洲 和玉 戎璐 王祥凯 ZHANG Yazhou;HE Yu;RONG Lu;WANG Xiangkai(Software Engineering College,Zhengzhou University of Light Industry,Zhengzhou 450001,Henan,China;Human Resources Office,Zhengzhou University of Light Industry,Zhengzhou 450001,Henan,China;Shandong Zhengyun Information Technology Co.,Ltd.,Jinan 250104,Shandong,China)

机构地区：[1]郑州轻工业大学软件学院,河南郑州450001 [2]郑州轻工业大学人事处,河南郑州450001 [3]山东正云信息科技有限公司,山东济南250104

出　　处：《计算机工程》2024年第8期75-85,共11页Computer Engineering

基　　金：国家自然科学基金青年基金项目(62006212);河南省科技攻关研究项目(222102210031);中国博士后科学基金面上项目(2023M733907)。

摘　　要：抑郁症作为一种常见的心理健康问题,严重影响人们的日常生活甚至是生命安全。鉴于目前的抑郁症检测存在主观性和人工干预等缺点,基于深度学习的自动检测方式成为热门研究方向。对于最易获取的文本模态而言,主要的挑战在于如何建模抑郁文本中的长距离依赖与序列依赖。为解决该问题,提出一种基于上下文知识的增强型Transformer网络模型RoBERTa-BiLSTM,旨在从抑郁文本序列中充分提取和利用上下文特征。结合序列模型与Transformer模型优点,建模单词间上下文交互,为抑郁类别揭示与信息表征提供参考。首先,利用RoBERTa方法将词汇嵌入到语义向量空间;其次,利用双向长短期记忆网络(BiLSTM)模型有效捕获长距离上下文语义;最后,在DAIC-WOZ和EATD-Corpus 2个大规模数据集上进行实证研究。实验结果显示,RoBERTa-BiLSTM模型的准确率分别达到0.74和0.93以上,召回率分别达到0.66和0.56以上,能够准确地检测抑郁症。Depression,as a prevalent mental health problem,substantially impacts individual's daily lives and wellbeing.Addressing the limitations of current depression detection,such as subjectivity and manual intervention,automatic detection methods based on deep learning have become a popular research direction.The primary challenge in the most accessible text modality is modelling the long-range and sequence dependencies in depressive texts.To address this problem,this paper proposes a contextual knowledge-based enhanced Transformer network model,named Robustly optimized Bidirectional Encoder Representations from Transformers approach-Bidirectional Long Short-Term Memory(RoBERTa-BiLSTM),to comprehensively extract and utilize contextual features from depressive text sequences.By combining the strengths of sequence models and Transformer architectures,the proposed model captures contextual interactions between words to provide a reference for depression category prediction and information characterization.First,the RoBERTa model is employed to embed vocabulary into a semantic vector space,and then,a BiLSTM network effectively captures long-range contextual semantics.Finally,empirical research is conducted on two large-scale datasets,DAIC-WOZ and EATD-Corpus.Experimental results demonstrate that the model achieves an accuracy exceeding 0.74 and 0.93,and a recall exceeding 0.66 and 0.56,respectively,enabling accurate depression detection.

关 键 词：抑郁检测 序列模型 深度学习 Transformer模型 双向长短期记忆模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]