7 papers
Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation
Slimane Bellaouar, Attia Nehar, Soumia Souffi +1
Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces the challenge of being under-resourced. The scarcity of large annotated datasets…
Arabic Multimodal Machine Learning: Datasets, Applications, Approaches, and Challenges
Abdelhamid Haouhat, Slimane Bellaouar, Attia Nehar +2
Multimodal Machine Learning (MML) aims to integrate and analyze information from diverse modalities, such as text, audio, and visuals, enabling machines to address complex tasks li…
A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detection
Abdelfateh Bekkair, Slimane Bellaouar, Slimane Oulad-Naoui
Community detection in networks with overlapping structures remains a significant challenge, particularly in noisy real-world environments where integrating topology, node attribut…
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Abdelfateh Bekkair, Slimane Bellaouar, Slimane Oulad-Naoui
Several natural phenomena and complex systems are often represented as networks. Discovering their community structure is a fundamental task for understanding these networks. Many…
Modality Influence in Multimodal Machine Learning
Abdelhamid Haouhat, Slimane Bellaouar, Attia Nehar +1
Multimodal Machine Learning has emerged as a prominent research direction across various applications such as Sentiment Analysis, Emotion Recognition, Machine Translation, Hate Spe…
Towards Arabic Multimodal Dataset for Sentiment Analysis
Abdelhamid Haouhat, Slimane Bellaouar, Attia Nehar +1
Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are centr…
Efficient Geometric-based Computation of the String Subsequence Kernel
Slimane Bellaouar, Hadda Cherroun, Djelloul Ziadi
Kernel methods are powerful tools in machine learning. They have to be computationally efficient. In this paper, we present a novel Geometric-based approach to compute efficiently…