Year: 2026
Type: conference-paper
Abstract: This system paper presents the approach of Team TONI-NLP to the PsyDefDetect 2026 shared task.The objective of the task was to classify utterances from helper-seeker conversations into nine categories: seven labels representing progressively higher levels of defensive maturity, one label indicating the absence of a defense mechanism, and one label for cases requiring additional information.We investigated several modern NLP approaches, including prompt engineering, fine-tuning, hierarchical modeling and classification using text embeddings derived from transformer-based models as well as classical embeddings such as TF-IDF.Our results show that ensemble methods performed best among our submitted systems, achieving a macro-F1 score of 0.320 and ranking 9th in the shared task out of 21 teams.
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Award: Machine Learning Core
OpenAlex ID: W7166877994