3 papers
cs.CL2026
CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
Menna Elgabry, Ali Hamdi, Khaled Shaban
Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this stu…
cs.LG2026
Enhancing Mental Health Classification with Layer-Attentive Residuals and Contrastive Feature Learning
Menna Elgabry, Ali Hamdi, Khaled Shaban
The classification of mental health is challenging for a variety of reasons. For one, there is overlap between the mental health issues. In addition, the signs of mental health iss…
cs.CL2025
Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion Detection
Menna Elgabry, Ali Hamdi
This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet's Jury Theorem (CJT). Unlike conventional…