Papers by Mohamed Hedi Maaloul
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Numerical Learning-Based Method for the Automatic Processing of Complex Disfluencies in Spontaneous Arabic Speech
2018 JCCO Joint International Conference on ICT in Education and Training, International Conference on Computing in Arabic, and International Conference on Geocomputing (JCCO: TICET-ICCA-GECO), 2018
In this paper, we propose a numerical learning-based method for the automatic processing of compl... more In this paper, we propose a numerical learning-based method for the automatic processing of complex disfluencies in spontaneous oral Arabic utterances. This method Allows, from a pretreated and semantically labeled utterance, to delimit and label the conceptual segments of a processed utterance. Also, it allows, from a segmented utterance, to detect and delimit the disfluent segments and then correct them. This work is a part of the realization of the Arabic vocal server SARF [2]. Thus, we implemented the complex disfluencies processing module (MTDC). The evaluation of the MTDC gave us satisfactory results with an F-measure equal to 91.9%. After integrating the MTDC into the SARF system, we achieved an improvement of 11.88% in acceptable understanding and a 3.77% decrease in error rate.
Identification of the Algiers Dialect Using the Linguistic Rules
2018 JCCO Joint International Conference on ICT in Education and Training, International Conference on Computing in Arabic, and International Conference on Geocomputing (JCCO: TICET-ICCA-GECO), 2018
In this article, we present a symbolic method for the identification of the Algerian dialect. The... more In this article, we present a symbolic method for the identification of the Algerian dialect. The method we propose is based essentially on decision rules extracted from our transcribed corpus ALGDC (ALGerian Dialect Corpus). Thus, we intend to present in this article, and in the first place, the technique adopted for the collection of oral utterances and the method followed for their transcription. Second, we will aim to explore the decision rules based on a deep morphological analysis and on linguistic clues. Thirdly, we will consider the revision of the information extracted from the decision rules by implemented correction rules. The totality of the information deduced can then help to deduce a decision concerning the type of dialect and more precisely the Algerian one.
Real-Time, CNN-Based Assistive Device for Visually Impaired People
2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2021
Visual impairment limits people's ability to move about unaided and interact with the surroun... more Visual impairment limits people's ability to move about unaided and interact with the surrounding world. This paper aims to leverage recent advances in deep learning to assist visually impaired people in their daily challenges. The high accuracy of deep learning comes at the expense of high computational requirements for both the training and the inference phases. To meet the computational requirements of deep learning, a common approach is to move data from the assistive device to distant servers (i.e. cloud-based inference). Such data movement requires a fast and active network connection and raises latency, cost, and privacy issues. In contrast with most of exiting assistive devices, in our work we move the computation to where data resides and opt for an approach where inference is performed directly “on” device (i.e. on-device-based inference). Running state-of-the-art deep learning models for a real-time inference on devices with limited resources is a challenging problem that cannot be solved without trading accuracy for speed (no free lunch). In this paper we conduct an extensive experimental study of 12 state-of-the-art object detectors, to strike the best trade-off between speed and accuracy. Our experimental study shows that by choosing the right models, frameworks, and compression techniques, we can achieve decent inference speed with very low accuracy drop.
We present in this paper an automatic summarization technique of Arabic texts, based on RST. We f... more We present in this paper an automatic summarization technique of Arabic texts, based on RST. We first present a corpus study which enabled us to specify, following empirical observations, a set of relations and rhetorical frames. Then, we present our method to automatically summarize Arabic texts. Finally, we present the architecture of the ARSTResume system. This method is based on the Rhetorical Structure Theory (Mann, 1988) and uses linguistic knowledge. The method relies on three pillars. The first consists in locating the rhetorical relations between the minimal units of the text by applying the rhetorical rules. One of these units is the nucleus (the segment necessary to maintain coherence) and the other can be either nucleus or satellite (an optional segment). The second pillar is the representation and the simplification of the RST-tree that represents the entries text in hierarchical form. The third pillar is the selection of sentences for the final summary, which takes int...
Mes dédicaces ne sont que l'expression de mes profondes gratitudes, de mes salutations chaleureus... more Mes dédicaces ne sont que l'expression de mes profondes gratitudes, de mes salutations chaleureuses et de ma sincère reconnaissance à tous ceux qui comblent ma vie et y confèrent son goût et sa saveur. Je dédie cette thèse à : Mon père Ce symbole de sacrifice et de dévouement, ses aides et ses recommandations m'ont souvent incité à persévérer dans l'effort et à progresser dans ma vie universitaire et professionnelle. Ma mère Ce rayon de soleil qui ne cesse d'éclairer ma vie, cette source inépuisable d'amour, de tendresse et d'affection, grâce à ses conseils, j'ai pu frayer mon chemin et savourer le goût de la réussite et du succès.
Automatic summarization of Semitic languages
This chapter addresses automatic summarization of Semitic languages. After a presentation of the ... more This chapter addresses automatic summarization of Semitic languages. After a presentation of the theoretical background and current challenges of the automatic summarization, we present different approaches suggested to cope with these challenges. These approaches fall on to two classes: single vs. multiple document summarization approaches. The main approaches dealing with Semitic languages (mainly Arabic, Hebrew, Maltese and Amharic) are then discussed. Finally, a case study of a specific Arabic automatic summarization system is presented. The summary section draws the most insightful conclusions and discusses some future research directions
Digital Learning for Summarizing Arabic Documents
Lecture Notes in Computer Science, 2010
... Mohamed Mahdi Boudabous1, Mohamed Hédi Maaloul2, and Lamia Hadrich Belguith1 ... Université d... more ... Mohamed Mahdi Boudabous1, Mohamed Hédi Maaloul2, and Lamia Hadrich Belguith1 ... Université de Provence - 5 Avenue Pasteur 13604 Aix en Provence France mahdiboudabous@gmail. com, [email protected] ... 80 MM Boudabous, MH Maaloul, and LH Belguith ...
Theory and Applications of Natural Language Processing, 2014
Iskandar Keskes Mohamed Mahdi Boudabous Mohamed Hédi Maaloul Lamia Hadrich Belguith (1) ANLP Rese... more Iskandar Keskes Mohamed Mahdi Boudabous Mohamed Hédi Maaloul Lamia Hadrich Belguith (1) ANLP Research Group, Laboratoire MIRACL, Route de Tunis Km 10, BP 242, Sfax, Tunisie (2) Laboratoire IRIT, 118 Route de Narbonne, F-31062 Toulouse Cedex 9, France (3) Laboratoire LPL, 5 avenue Pasteur, BP 80975, 13604 Aix-en-Provence, France [email protected], [email protected] [email protected], [email protected]
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Papers by Mohamed Hedi Maaloul