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Your search keyword '"Lakhouaja, Abdelhak"' showing total 5 results
Start Over Author "Lakhouaja, Abdelhak" Topic arabic
5 results on '"Lakhouaja, Abdelhak"'

1. 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. A Comparative Study of Standard Part-of-Speech Taggers

Author

Zeroual, Imad, Lakhouaja, Abdelhak, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Ezziyyani, Mostafa, editor

Published
2019
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4. 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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5. Adapting a decision tree based tagger for Arabic

Author

Lakhouaja Abdelhak and Zeroual Imad

Subjects
business.industry, Arabic, Computer science, Speech recognition, Lemmatisation, Decision tree, 02 engineering and technology, computer.software_genre, Part of speech, language.human_language, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, language, Modern Standard Arabic, 020201 artificial intelligence & image processing, Artificial intelligence, Language model, 0305 other medical science, Hidden Markov model, business, Classical Arabic, computer, Natural language processing, Lemma (morphology)
Abstract

Several probabilistic methods used for Part of speech (POS) tagging are based on Hidden Markov Models (HMM), these methods have difficulties especially in estimating transition probabilities accurately from limited amounts of training data. Consequently, a new method appeared to avoid problems that HMM face. However, the transition probabilities are estimated using a decision tree. Based on this method a language independent POS tagger (called TreeTagger) has been implemented. The main purpose of this work is to create the language model to adapt TreeTagger for Arabic POS tagging and lemmatization. Furthermore, different configurations have been done, namely, collecting lexical resources, as well as the annotated training corpora. In addition, we used the proposed universal tagset that consists of common POS categories of 22 different languages including Arabic. We highlight the use of this tagger via various experiments on vowelled and unvowelled text from both Modern Standard Arabic and Classical Arabic. In fact, the obtained accuracies rates are 99.4%, 92.6% and 81.9% for respectively the Quranic vowelled corpus "Al-Mus'haf", the unvowelled "Al-Mus'haf1" corpus and for the NEMLAR corpus.

Published
2016
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