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20232025
most citedMedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations

3 citations · 4 across the 6 of their papers we have counts for

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cs.CL2025

Hope Speech Detection in Social Media English Corpora: Performance of Traditional and Transformer Models

Luis Ramos, Hiram Calvo, Olga Kolesnikova

The identification of hope speech has become a promised NLP task, considering the need to detect motivational expressions of agency and goal-directed behaviour on social media plat…

cs.CL2025

Online Social Support Detection in Spanish Social Media Texts

Moein Shahiki Tash, Luis Ramos, Zahra Ahani +4

The advent of social media has transformed communication, enabling individuals to share their experiences, seek support, and participate in diverse discussions. While extensive res…

cs.CL2024

Stress Detection on Code-Mixed Texts in Dravidian Languages using Machine Learning

L. Ramos, M. Shahiki-Tash, Z. Ahani +3

Stress is a common feeling in daily life, but it can affect mental well-being in some situations, the development of robust detection models is imperative. This study introduces a…

cs.CL2024★ 1 cited

Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations

Olumide Ebenezer Ojo, Olaronke Oluwayemisi Adebanji, Alexander Gelbukh +2

Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generate…

cs.CL2023

Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model

Olumide E. Ojo, Olaronke O. Adebanji, Hiram Calvo +5

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying…

cs.CL2023★ 3 cited

MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations

Olumide E. Ojo, Olaronke O. Adebanji, Alexander Gelbukh +2

Zero-shot classification enables text to be classified into classes not seen during training. In this study, we examine the efficacy of zero-shot learning models in classifying hea…