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Evaluating Lexicon Incorporation for Depression Symptom Estimation

arXiv 2404.19359 ↗First posted 30 Apr 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 30 Apr 2024.

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References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
  3. 2020On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselinescited by 254
  4. 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 193
  5. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 159
  6. 2021An Improved Baseline for Sentence-level Relation Extractioncited by 80
  7. 2020Improving BERT with Syntax-aware Local Attentioncited by 26
  8. 2020Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Frameworkcited by 25
  9. 2017Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Mediacited by 24
  10. 2022Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Postscited by 23
  11. 2022Multi-modal Depression Estimation based on Sub-attentional Fusioncited by 15
  12. 2023Emotion fusion for mental illness detection from social media: A surveycited by 13
4 more
  1. 2020Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviewscited by 8
  2. 2023Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviewscited by 7
  3. 2022Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guidingcited by 6
  4. 2024Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Datasetcited by 3

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