2019/04/08 by Heinrich Dinkel, Dinkel, Heinrich, Mengyue Wu +3 · 27 citations
Computer Science · Psychology · #Advanced Text Analysis Techniques #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Data science #Depression (economics) #Economics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Keynesian economics #Machine Learning (cs.LG) #Mental Health via Writing #Psychology #Sentiment Analysis and Opinion Mining #cs.CL #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.1904.05154
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/04/08 · arxiv created 2020/07/08 · arxiv updated 2020/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Previous text-based depression detection is commonly based on large user-generated data. Sparse scenarios like clinical conversations are less investigated. This work proposes a text-based multi-task BGRU network with pretrained word embeddings to model patients' responses during clinical interviews. Our main approach uses a novel multi-task loss function, aiming at modeling both depression severity and binary health state. We independently investigate word- and sentence-level word-embeddings as well as the use of large-data pretraining for depression detection. To strengthen our findings, we report mean-averaged results for a multitude of independent runs on sparse data. First, we show that pretraining is helpful for word-level text-based depression detection. Second, our results demonstrate that sentence-level word-embeddings should be mostly preferred over word-level ones. While the choice of pooling function is less crucial, mean and attention pooling should be preferred over last-timestep pooling. Our method outputs depression presence results as well as predicted severity score, culminating a macro F1 score of 0.84 and MAE of 3.48 on the DAIC-WOZ development set.