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Your search keyword '"Lakhouaja, Abdelhak"' showing total 9 results
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9 results on '"Lakhouaja, Abdelhak"'

1. Optimizing Arabic Text Readability Measurement Using Morphological Word Embeddings

Author

Berrichi, Safae, Nassiri, Naoual, Mazroui, Azzeddine, Lakhouaja, Abdelhak, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Elhadj, Yahya Mohamed, editor, Nanne, Mohamedade Farouk, editor, Koubaa, Anis, editor, Meziane, Farid, editor, and Deriche, Mohamed, editor

Published
2024
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2. Exploring the Impact of Deep Learning Techniques on Evaluating Arabic L1 Readability

Author

Berrichi, Safae, Nassiri, Naoual, Mazroui, Azzeddine, Lakhouaja, Abdelhak, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Farhaoui, Yousef, editor, Hussain, Amir, editor, Saba, Tanzila, editor, Taherdoost, Hamed, editor, and Verma, Anshul, editor

Published
2024
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3. Lexical Simplification of Arabic Educational Texts Through a Classification Approach

Author

Nassiri, Naoual, Berrichi, Safae, Lakhouaja, Abdelhak, Cavalli-Sforza, Violetta, Mazroui, Azzeddine, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Farhaoui, Yousef, editor, Rocha, Alvaro, editor, Brahmia, Zouhaier, editor, and Bhushab, Bharat, editor

Published
2023
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4. Impact of Feature Vectorization Methods on Arabic Text Readability Assessment

Author

Berrichi, Safae, Nassiri, Naoual, Mazroui, Azzeddine, Lakhouaja, Abdelhak, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Farhaoui, Yousef, editor, Rocha, Alvaro, editor, Brahmia, Zouhaier, editor, and Bhushab, Bharat, editor

Published
2023
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6. Modern Standard Arabic Readability Prediction

Author

Nassiri, Naoual, Lakhouaja, Abdelhak, Cavalli-Sforza, Violetta, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Lachkar, Abdelmonaime, editor, Bouzoubaa, Karim, editor, Mazroui, Azzedine, editor, Hamdani, Abdelfettah, editor, and Lekhouaja, Abdelhak, editor

Published
2018
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7. Arabic Readability Assessment for Foreign Language Learners

Author

Nassiri, Naoual, Lakhouaja, Abdelhak, Cavalli-Sforza, Violetta, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Silberztein, Max, editor, Atigui, Faten, editor, Kornyshova, Elena, editor, Métais, Elisabeth, editor, and Meziane, Farid, editor

Published
2018
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8. INTERPRETING THE RELEVANCE OF READABILITY PREDICTION FEATURES.

Author

Berrichi, Safae, Nassiri, Naoual, Mazroui, Azzeddine, and Lakhouaja, Abdelhak

Subjects
ARABIC language, FOREIGN language education, FEATURE selection, MACHINE learning, SUPPORT vector machines
Abstract

Text readability is one of the main research areas widely developed in several languages, but it is highly limited when dealing with the Arabic language. The main challenge in this area is to identify an optimal set of features that represent texts and allow us to evaluate their readability level. To address this challenge, we propose in this study various feature selection methods that can significantly retrieve the set of discriminating features representing Arabic texts. The second aim of this paper is to evaluate different sentence-embedding approaches (ArabicBert, AraBert and XLM-R) and compare their performances to those obtained using the selected linguistic features. We performed experiments with both SVM and Random Forest classifiers on two different corpora dedicated to learning Arabic as a foreign language (L2). The obtained results show that reducing the number of features improves the performance of the readability-prediction models by more than 25% and 16% for the two adopted corpora, respectively. In addition, the fine-tuned Arabic-BERT model performs better than the other sentence-embedding methods, but it provided less improvement than the feature-based models. Combining these methods with the most discriminating features produced the best performance. [ABSTRACT FROM AUTHOR]

Published
2023

9. Arabic L2 readability assessment: Dimensionality reduction study.

Author

Nassiri, Naoual, Lakhouaja, Abdelhak, and Cavalli-Sforza, Violetta

Subjects
ARABIC language, LANGUAGE & languages, LINEAR programming
Abstract

Readability is a measure that associates a written text to a reader's skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning. The complexity of reading in some languages, such as Arabic, presents a great challenge to overcome and increases the need for such a measure before giving texts to a learner. In order to develop powerful tools that automate this choice, we must first choose the optimal feature set to be used in the development of such a tool. In this paper, we present an approach to automatically measure the readability of Arabic as a foreign language through a series of experiments. We begin by using a wide range of features plausibly relevant to readability, as found in the literature, and reduce them in subsequent experiments by eliminating features that appear to have little significance in readability prediction. Our objective is to keep the smallest set of features that gives good readability prediction accuracy based on three different corpora for learners of Arabic as a foreign language, annotated with five difficulty levels. Our best L2 readability accuracy result is 86.15%. [ABSTRACT FROM AUTHOR]

Published
2022
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