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El Moatez Billah Nagoudi
Researcher at University of British Columbia
Publications - 42
Citations - 386
El Moatez Billah Nagoudi is an academic researcher from University of British Columbia. The author has contributed to research in topics: Machine translation & Computer science. The author has an hindex of 7, co-authored 33 publications receiving 224 citations.
Papers
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Proceedings ArticleDOI
Semantic Similarity of Arabic Sentences with Word Embeddings
TL;DR: An innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences by exploiting vectors as word representations in a multidimensional space in order to capture the semantic and syntactic properties of words.
Proceedings ArticleDOI
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
TL;DR: The authors introduced two powerful deep bidirectional transformer-based models, ARBERT and MARBERT, for multi-dialectal Arabic language understanding evaluation, which achieved state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets).
Posted Content
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic.
TL;DR: The authors introduced two powerful deep bidirectional transformer-based models, ARBERT and MARBERT, for multi-dialectal Arabic language understanding evaluation, which achieved state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets).
Journal ArticleDOI
A Flexible Encryption Technique for the Internet of Things Environment
Saci Medileh,Abdelkader Laouid,El Moatez Billah Nagoudi,Reinhardt Euler,Ahcène Bounceur,Mohammad Hammoudeh,Muath AlShaikh,Amna Eleyan,Osama Ahmed Khashan +8 more
TL;DR: A new scalable encryption technique, called FlexenTech, to protect IoT data during storage and in transit, which offers a low encryption time, defends against common attacks such as replay attacks and defines a configurable mode, where any number of rounds or key sizes may be used.
Book ChapterDOI
Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences
TL;DR: Two word embedding-based approaches devoted to measuring the semantic similarity between Arabic-English cross-language sentences are proposed and the proposed methods are confirmed through the Pearson correlation between the similarity scores and human ratings.