no code implementations • LTEDI (ACL) 2022 • Nawshad Farruque, Osmar Zaiane, Randy Goebel, Sudhakar Sivapalan
In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
no code implementations • 28 Oct 2022 • Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan, Osmar R. Zaïane
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts.
no code implementations • 6 Sep 2022 • Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan, Osmar Zaiane
In our work, we describe a Semi-supervised Learning (SSL) framework which uses an initial supervised learning model that leverages 1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated Depression Tweets Repository (DTR).
no code implementations • 21 Jun 2021 • Nawshad Farruque, Randy Goebel, Osmar Zaiane, Sudhakar Sivapalan
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i. e. Depression Symptoms Detection (DSD) from text.