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2022.lrec-1.343.pdf
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🔗 Source: aclanthology.org
📊 Size: 352 KB
👤 Author: Ana Sabina Uban ; Berta Chulvi ; Paolo Rosso
⬇️ Downloads: 201

Summary

Researchers propose a multi-aspect transfer learning approach for detecting low-resource mental disorders on social media, targeting depression, PTSD, anorexia, and self-harm. They explore deep learning models with interpretable linguistic features and transfer learning strategies for cross-disorder and cross-platform transfer, showing that transfer learning can improve prediction performance for disorders with limited annotated data. The study aims to answer three research questions: (RQ1) can transfer learning improve detection performance for scarce datasets and across social media platforms, (RQ2) what can be learned about disorder similarity through transfer learning, and (RQ3) how can interpretable models reveal linguistic dimensions similar across disorders. The researchers contribute to the automatic detection of mental disorders by using social media post datasets, experimenting with deep learning models, and combining a deep learning architecture with a multi-aspect representation at the feature level.

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Researchers propose a multi-aspect transfer learning approach for detecting low-resource mental disorders on social media, targeting depression, PTSD,...

Technical Information

  • File Format: PDF
  • File Size: 352 KB
  • Pages: 18
  • Language: EN
  • Author: Ana Sabina Uban ; Berta Chulvi ; Paolo Rosso
  • Total Downloads: 201
  • Last Updated: 1 month ago

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