Multi-Task Learning to Capture Changes in Mood Over Time

no code implementations • NAACL (CLPsych) 2022 • Prasadith Kirinde Gamaarachchige, Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen

This paper investigates the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user).

Ensemble Learning Multi-Task Learning

Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text

no code implementations • COLING 2018 • Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, David Van Bruwaene

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL).

General Classification Multi-Task Learning +2

Deep Learning for Depression Detection of Twitter Users

no code implementations • WS 2018 • Ahmed Husseini Orabi, Prasadith Buddhitha, Mahmoud Husseini Orabi, Diana Inkpen

Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders.

Depression Detection

uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network

no code implementations • SEMEVAL 2018 • Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene

We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets.

regression