From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP
Jan 1, 2024
Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk
We combined theory-driven suicidal archetypes with language models and relative entropy to identify suicide risk in text. The combined approach outperformed individual methods, achieving high accuracy in shared task evaluations. Our findings suggest that integrating theoretical and data-driven methods is crucial for mental health analysis, outperforming prompt-based approaches.
Jan 1, 2024
ALBA: Adaptive Language-Based Assessments for Mental Health
This study introduces Adaptive Language-Based Assessment (ALBA) for mental health, using limited language responses to adaptively order questions and assess psychological traits. Two methods, ALIRT and Actor-Critic, outperformed non-adaptive baselines. ALIRT proved most effective, achieving high accuracy with fewer questions, demonstrating that adaptive assessments can maintain validity with smaller language samples and lower computational costs.
Jan 1, 2024
Discourse-level representations can improve prediction of degree of anxiety
Jan 1, 2023
\" I Slept Like a Baby\": Using Human Traits To Characterize Deceptive ChatGPT and Human Text.
Jan 1, 2023
WWBP-SQT-lite: Multi-level models and difference embeddings for moments of change identification in mental health forums
Jan 1, 2022
Detecting dissonant stance in social media: The role of topic exposure
Jan 1, 2022