most citedDepression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach

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cs.CL20233 cited

Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task

Ha-Thanh Nguyen, Randy Goebel, Francesca Toni +2

The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal text…

cs.CL2023

A negation detection assessment of GPTs: analysis with the xNot360 dataset

Ha Thanh Nguyen, Randy Goebel, Francesca Toni +2

Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generativ…

cs.CL20223 cited

Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach

Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1

A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD…

cs.CL2021

A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language

Nawshad Farruque, Randy Goebel, Osmar Zaiane

We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection…

cs.CL2021

STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text

Nawshad Farruque, Randy Goebel, Osmar Zaiane +1

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 s…