Relevant bibliographies by topics / Arabic preprocessing

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Arabic preprocessing'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Arabic preprocessing"

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Hegazi, Mohamed Osman, Yasser Al-Dossari, Abdullah Al-Yahy, Abdulaziz Al-Sumari, and Anwer Hilal. "Preprocessing Arabic text on social media." Heliyon 7, no. 2 (2021): e06191. http://dx.doi.org/10.1016/j.heliyon.2021.e06191.

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Ayedh, Abdullah, Guanzheng TAN, Khaled Alwesabi, and Hamdi Rajeh. "The Effect of Preprocessing on Arabic Document Categorization." Algorithms 9, no. 2 (2016): 27. http://dx.doi.org/10.3390/a9020027.

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Ahmed, Rawia I. O., and Mohamed E. M. Musa. "Preprocessing Phase for Offline Arabic Handwritten Character Recognition." International Journal of Computer Applications Technology and Research 5, no. 12 (2016): 760–63. http://dx.doi.org/10.7753/ijcatr0512.1005.

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Nassr, Z., N. Sael, and F. Benabbou. "PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 323–30. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-323-2020.

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Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentimen
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salim, marwa, sally Saad, and mostafa aref. "PREPROCESSING THE EGYPTIAN ARABIC DIALECT FOR PERSONALITY TRAITS PREDICTION." International Journal of Intelligent Computing and Information Sciences 19, no. 1 (2019): 1–12. http://dx.doi.org/10.21608/ijicis.2019.62603.

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Oussous, Ahmed, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih. "ASA: A framework for Arabic sentiment analysis." Journal of Information Science 46, no. 4 (2019): 544–59. http://dx.doi.org/10.1177/0165551519849516.

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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these c
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Mhamed, Mustafa, Richard Sutcliffe, Xia Sun, Jun Feng, Eiad Almekhlafi, and Ephrem Afele Retta. "Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing." Computational Intelligence and Neuroscience 2021 (September 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/5538791.

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Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et a
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Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." International Journal of Computer Vision and Image Processing 1, no. 2 (2011): 58–77. http://dx.doi.org/10.4018/ijcvip.2011040105.

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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting
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Luqman, Hamzah, Sabri A. Mahmoud, and Sameh Awaida. "Arabic and Farsi Font Recognition: Survey." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 01 (2015): 1553002. http://dx.doi.org/10.1142/s021800141553002x.

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Font Recognition (FR) is useful in improving optical text recognition accuracy and time. In addition, it can be used to restore the original document text fonts, styles and sizes. In this paper, we survey the literature of Arabic and Farsi FR research and used databases. The main phases of FR systems are surveyed (viz. preprocessing, classification techniques and used features). All published work of Arabic and Farsi FR, which the authors are aware of, are surveyed. To our knowledge, this is the first survey of Arabic/Farsi FR and used databases. In addition, the paper addresses the strengths
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Manal Nejjari and Abdelouafi Meziane. "SSAAR: An enhanced System for Sentiment Analysis of Arabic Reviews." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (August 17, 2020): 81–95. http://dx.doi.org/10.24297/ijct.v20i.8827.

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Sentiment Analysis, or Opinion Mining, has recently captivated the interest of scientists worldwide. With the increasing use of the internet, the web is becoming overloaded by data that contains useful information, which can be used in different fields. In fact, many studies have shed light on Sentiment Analysis of online data in different languages. However, the amount of research dealing with the Arabic language is still limited. In this paper, an empirical study is led to Sentiment Analysis of online reviews written in Modern Standard Arabic. A new system called SSAAR (System for Sentiment
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Dissertations / Theses on the topic "Arabic preprocessing"

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Lebboss, Georges. "Contribution à l’analyse sémantique des textes arabes." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080046/document.

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La langue arabe est pauvre en ressources sémantiques électroniques. Il y a bien la ressource Arabic WordNet, mais il est pauvre en mots et en relations. Cette thèse porte sur l’enrichissement d’Arabic WordNet par des synsets (un synset est un ensemble de mots synonymes) à partir d’un corpus général de grande taille. Ce type de corpus n’existe pas en arabe, il a donc fallu le construire, avant de lui faire subir un certain nombre de prétraitements.Nous avons élaboré, Gilles Bernard et moi-même, une méthode de vectorisation des mots, GraPaVec, qui puisse servir ici. J’ai donc construit un systèm
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Gahbiche-Braham, Souhir. "Amélioration des systèmes de traduction par analyse linguistique et thématique : Application à la traduction depuis l'arabe." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00878887.

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La traduction automatique des documents est considérée comme l'une des tâches les plus difficiles en traitement automatique des langues et de la parole. Les particularités linguistiques de certaines langues, comme la langue arabe, rendent la tâche de traduction automatique plus difficile. Notre objectif dans cette thèse est d'améliorer les systèmes de traduction de l'arabe vers le français et vers l'anglais. Nous proposons donc une étude détaillée sur ces systèmes. Les principales recherches portent à la fois sur la construction de corpus parallèles, le prétraitement de l'arabe et sur l'adapta
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Book chapters on the topic "Arabic preprocessing"

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Habash, Nizar, and Fatiha Sadat. "Arabic preprocessing for Statistical Machine Translation." In Challenges for Arabic Machine Translation. John Benjamins Publishing Company, 2012. http://dx.doi.org/10.1075/nlp.9.05hab.

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Zbib, Rabih, and Ibrahim Badr. "Preprocessing for English-to-Arabic Statistical Machine Translation." In Challenges for Arabic Machine Translation. John Benjamins Publishing Company, 2012. http://dx.doi.org/10.1075/nlp.9.06zbi.

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Maghfour, Mohcine, and Abdeljalil Elouardighi. "Toward Improving Arabic Text Preprocessing in Sentiment Analysis." In Digital Technologies and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73882-2_63.

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Sadat, Fatiha, and Emad Mohamed. "Improved Arabic-French Machine Translation through Preprocessing Schemes and Language Analysis." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_31.

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Elbarougy, Reda, Gamal Behery, and Akram El Khatib. "A Proposed Natural Language Processing Preprocessing Procedures for Enhancing Arabic Text Summarization." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34614-0_3.

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Sahlol, Ahmed T., Ching Y. Suen, Mohammed R. Elbasyoni, and Abdelhay A. Sallam. "Investigating of Preprocessing Techniques and Novel Features in Recognition of Handwritten Arabic Characters." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-11656-3_24.

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Daneshfar, F., W. Fathy, and B. Alaqeband. "A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch027.

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Preprocessing is a very important part of cursive languages Optical Character Recognition (OCR) systems. Thus, baseline detection, which is one of the main parts of the preprocessing operation, plays a basic role on OCR systems; improvement on baseline detection could be absolutely useful for decreasing errors in recognition words. In this chapter, a metaheuristic- and mathematical-based algorithm is recommended, which has improved the baseline detection process in relation to the well-known baseline detection algorithms. The most important advantages of the proposed method are simplicity, high speed processing, and reliability. To test this novel solution, IFN/ENIT database, which is a well-known and attending database, is utilized. However, the proposed solution is reliable to any standard database of cursive language's OCR.
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Daneshfar, F., W. Fathy, and B. Alaqeband. "A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages." In Handbook of Research on Artificial Intelligence Techniques and Algorithms. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7258-1.ch023.

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Preprocessing is a very important part of cursive languages Optical Character Recognition (OCR) systems. Thus, baseline detection, which is one of the main parts of the preprocessing operation, plays a basic role on OCR systems; improvement on baseline detection could be absolutely useful for decreasing errors in recognition words. In this chapter, a metaheuristic- and mathematical-based algorithm is recommended, which has improved the baseline detection process in relation to the well-known baseline detection algorithms. The most important advantages of the proposed method are simplicity, high speed processing, and reliability. To test this novel solution, IFN/ENIT database, which is a well-known and attending database, is utilized. However, the proposed solution is reliable to any standard database of cursive language's OCR.
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Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." In Intelligent Computer Vision and Image Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3906-5.ch010.

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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.
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Hathlian, Nourah F. Bin, and Alaaeldin M. Hafez. "Subjective Text Mining for Arabic Social Media." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch075.

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The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.
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Conference papers on the topic "Arabic preprocessing"

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Chammas, Edgard, Chafic Mokbel, and Laurence Likforman-Sulem. "Arabic handwritten document preprocessing and recognition." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333802.

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El Isbihani, Anas, Shahram Khadivi, Oliver Bender, and Hermann Ney. "Morpho-syntactic Arabic preprocessing for Arabic-to-English statistical machine translation." In the Workshop. Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1654650.1654654.

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Boukerma, Hanene, and Nadir Farah. "Preprocessing Algorithms for Arabic Handwriting Recognition Systems." In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2012. http://dx.doi.org/10.1109/acsat.2012.59.

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Habash, Nizar, and Fatiha Sadat. "Arabic preprocessing schemes for statistical machine translation." In the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers. Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1614049.1614062.

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AbdElNafea, Mohamed, and Samia Heshmat. "Efficient Preprocessing Algorithm for Online Handwritten Arabic Strokes." In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). IEEE, 2019. http://dx.doi.org/10.1109/itce.2019.8646643.

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Boussellaa, W., A. Zahour, B. Taconet, A. Alimi, and A. Benabdelhafid. "PRAAD: Preprocessing and Analysis Tool for Arabic Ancient Documents." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377077.

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Sadat, Fatiha, and Nizar Habash. "Combination of Arabic preprocessing schemes for statistical machine translation." In the 21st International Conference. Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1220175.1220176.

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Abandah, Gheith A., and Ahmad S. Ai-Hourani. "Challenges and Preprocessing Recommendations for MADCAT Dataset of Handwritten Arabic Documents." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633103.

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Kavianifar, M., and A. Amin. "Preprocessing and structural feature extraction for a multi-fonts Arabic/Persian OCR." In Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318). IEEE, 1999. http://dx.doi.org/10.1109/icdar.1999.791762.

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Al-qerem, Ahmad, Ghazi Al-Naymat, and Mays Alhasan. "Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection." In 2019 International Arab Conference on Information Technology (ACIT). IEEE, 2019. http://dx.doi.org/10.1109/acit47987.2019.8991084.

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