Summary of Evaluating Lexicon Incorporation For Depression Symptom Estimation, by Kirill Milintsevich et al.
Evaluating Lexicon Incorporation for Depression Symptom Estimation
by Kirill Milintsevich, Gaël Dias, Kairit Sirts
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the effect of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for estimating depression symptoms from patient-therapist conversations and social media posts. The model marks words in transcripts and social media posts as part of various lexicons, showing that incorporating external knowledge can improve prediction performance. Different lexicons exhibit distinct behaviors depending on the task, with state-of-the-art results achieved for estimating depression levels from patient-therapist interviews. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special language models to predict how much someone is depressed based on conversations and social media posts. They added extra information about words that have certain emotions or feelings, which helps the model understand what people are saying better. The results show that using this extra information makes the predictions more accurate. Different types of information had different effects depending on what was being predicted. |
Keywords
» Artificial intelligence » Transformer
