Survey

Al-Emran, M., and K. Shaalan, "Learners and Educators Attitudes Towards Mobile Learning in Higher Education: State of the Art", Advances in Computing, Communications and Informatics (ICACCI), India, 11 August, 2015. Abstractstate_of_the_art_mobilelearning.pdf

In the last few years, the way we learn has been significantly changed from traditional classrooms that depend on printed papers into e-learning relying on electronic teaching material. Contemporary educational technologies attempt to facilitate the delivery of learning from instructors to students in a more flexible and comfortable way. Mobile learning (M-learning) is one of such pervasive technologies that has been evolved rapidly to deliver e-learning using personal electronic devices without posing any restrictions on time and location. Literature that sheds light on using M-learning in various institutions of learning is beginning to emerge. The work in this paper demonstrates the state of the art of the M-learning. It discusses learners’ and educators’ attitudes towards the use and adoption of M-learning. Advantages and disadvantages of M-learning were also presented. The integration and implementation of M-learning with other technological resources has been described. Factors affecting the students’ and faculty members’ attitudes towards the use of M-learning have been demonstrated. Moreover, the new trends and challenges, which are evolved while conducting this survey, are explained.

Emran, M. A., and K. Shaalan, "A Survey of Intelligent Language Tutoring Systems", Advances in Computing, Communications and Informatics (ICACCI’14), Delhi, India, 25 Septemper, 2014. Abstractsurvey_IELTS.pdf

Intelligent Languages Tutoring Systems (ILTSs) plays a significant role in evaluating students' answers through interaction with them. ILTSs implements Natural Language processing (NLP) techniques in order to allow free input of words and sentences. ILTSs have the capability of identifying the input errors and provide an immediate feedback along with the errors source. It has been observed that ILTSs were not surveyed intensively; the reason that motivates us to conduct this research. Some NLP recent trends such as Latent Sematic Analysis and entailment were demonstrated. Different ILTSs have been discussed with a dedicated section about the development of Arabic ILTSs. Arabic share many of its characteristics with Semitic and morphologically rich languages. In our presentation we point out new trends that have been emerged while conducting survey.

Shaalan, K., "A Survey of Arabic Named Entity Recognition and Classification", Computational LinguisticsComputational Linguistics, vol. 40, issue 2: MIT Press, pp. 469 - 510, 2013, 2014. Abstractcoli_a_00178.pdfWebsite

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

Chang, C. -hui, M. Kayed, M. Girgis, and K. Shaalan, "A Survey of Web Information Extraction Systems", IEEE Trans. on Knowl. and Data Eng., vol. 18, no. 10, Piscataway, NJ, USA, IEEE Educational Activities Department, pp. 1411–1428, oct, 2006. Abstractiesurvey2006.pdfWebsite

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches.

Farghaly, A., and K. Shaalan, "Arabic Natural Language Processing: Challenges and Solutions", ACM Transactions on Asian Language Information Processing (TALIP), vol. 8, no. 4, New York, NY, USA, ACM, pp. 1-22, 2009. Abstractfarghaly_shaalan_talip_anlp_pdf.pdfWebsite

The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.

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