论文标题
在转录的临床访谈中应用于抑郁症检测的层次网络上的情感条件
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
论文作者
论文摘要
在这项工作中，我们提出了一个机器学习模型，用于从转录的临床访谈中检测到抑郁症。抑郁症是一种精神障碍，不仅会影响受试者的情绪，而且会影响语言的使用。为此，我们使用分层注意力网络对沮丧受试者进行访谈。我们通过从情感词典中提取的语言特征的调理机制来增强模型的注意力层。我们的分析表明，被诊断出患有抑郁症的人在更大程度上使用情感语言而不是不沮丧。我们的实验表明，外部情感信息分别提高了一般心理治疗语料库和DAIC-WOZ 2017抑郁症数据集的拟议结构的性能，分别实现了最先进的71.6和68.6 F1分数。
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 68.6 F1 scores respectively.
