The concept of Theme is regarded as a functional linguistic element that exists in many languages. The main aim of this study is to explore the functions of Theme in Arabic, applying the Systemic Functional Linguistics framework adopted by Downing (1991). Methodologically, several related real examples have been selected from the written discourse of Modern Standard Arabic and then analyzed contextually. The empirical analysis has revealed that (i) Theme can provide different functions, such as Individual, Circumstantial and Subjective and Logical Frameworks for the interpretation of the Rheme, and (ii) Theme can interact dynamically with different grammatical functions (e.g. Subject, Object, etc.) and have different pragmatic functions (e.g. Topic, Given and New information). Therefore, the view that makes a necessary link between Theme on the one hand and Noun Phrase, Topic or Given information on the other hand is proven incorrect and empirically invalid. Similar results have been obtained in the context of English (Downing 1991) but not yet for Arabic? This strengthens not only the universality of the concept of Theme but also its functions.
A Smart City (SC) is a viable solution for green and sustainable living, especially with the current explosion in global population and rural-urban immigration. One of the fields that is not getting much attention in the Smart Economy (SE) is customer satisfaction. The SE is a component of SC that is concerned with using Information and Communication Technology (ICT) to improve stages of the traditional economy. In this paper, we propose a fog computing-based shopping recommendation system. Our simulations used Al-Madinah city as a case study. It aims to improve the customer shopping experience. Customers in shopping malls can connect to the system via Wi-Fi. Then the system recommends products to the shoppers according to their preferences. It optimizes shoppers’ schedules using price, the distance between the shops, and the congestion. It also improves customers’ savings by up to 30%. It also increases the shopping speed by up to 6.12% compared to the system proposed in the literature.
Arabic has recently received significant attention from corpus compilers. This situation has led to the creation of many Arabic corpora that cover various genres, most notably the newswire genre. Yet, Arabic novels, and specifically those authored by Saudi writers, lack the sufficient digital datasets that would enhance corpus linguistic and stylistic studies of these works. Thus, Arabic lags behind English and other European languages in this context. In this paper, we present the Saudi Novels Corpus, built to be a valuable resource for linguistic and stylistic research communities. We specifically present the procedures we followed and the decisions we made in creating the corpus. We describe and clarify the design criteria, data collection methods, process of annotation, and encoding. In addition, we present preliminary results that emerged from the analysis of the corpus content. We consider the work described in this paper as initial steps to bridge the existing gap between corpus linguistics and Arabic literary texts. Further work is planned to improve the quality of the corpus by adding advanced features.
Part of Speech (POS) tagging is one of the most common techniques used in natural language processing (NLP) applications and corpus linguistics. Various POS tagging tools have been developed for Arabic. These taggers differ in several aspects, such as in their modeling techniques, tag sets and training and testing data. In this paper we conduct a comparative study of five Arabic POS taggers, namely: Stanford Arabic, CAMeL Tools, Farasa, MADAMIRA and Arabic Linguistic Pipeline (ALP) which examine their performance using text samples from Saudi novels. The testing data has been extracted from different novels that represent different types of narrations. The main result we have obtained indicates that the ALP tagger performs better than others in this particular case, and that Adjective is the most frequent mistagged POS type as compared to Noun and Verb.
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