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Found 5 papers, 3 papers with code
Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023
To solve these tasks, we proposed models based on Transformers followed by a decision policy according to criteria defined by an early detection framework.
Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023
The CLEF eRisk Laboratory explores solutions to different tasks related to risk detection on the Internet.
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks.
t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale.
Ranked #1 on
Depression Detection
on eRisk 2017
A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
However, most standard and state-of-the-art supervised machine learning models are not well suited to deal with this scenario.
Ranked #2 on
Depression Detection
on eRisk 2017
