Arabic Text Research Articles

Examining the Cultural Connotations in Human and Machine Translations: A Corpus Study of Naguib Mahfouz's Zuqāq al-Midaqq

The translation of culture-specific terms constitutes a major challenge for professional translators as it necessitates a thorough understanding of both the linguistic and cultural elements. With rapid technological advancement over the past few years, machine translation has enhanced translation quality. This study investigates the transability of cultural connotations in the Arabic-English translation of Naguib Mahfouz’s Zuqāq al-Midaqq. A descriptive qualitative research design was adopted to achieve the intended goals of the study. The data comprised human and machine translations from Google Translate and ChatGPT. Through qualitative content analysis, the translations were compared for accuracy in transferring the cultural connotations prevalent in the Arabic source text. The findings revealed that the human translation showed greater naturalness and accuracy in rendering cultural connotations. Machine translation has struggled with rhetorical devices, idioms, and cultural nuances. The results also indicated that the AI-enhanced machine represented by ChatGPT captured the cultural elements more effectively than Google Translate. The study concluded that human expertise remains essential for the high-quality translation of literary works to maintain cultural significance. The findings can inform translator training and guide improvements to AI-enhanced translation for literary texts.

Translatability of Arabic Cultural References to English: A Case Study of The Queue Novel

Cultural references are phrases and words embedded within a source text that refer to entities, persons, or even ideas that are not present in the target language and culture or are significantly different in terms of sociocultural equivalents. Translating cultural references into a language where translated texts are interpreted through the prism of cultural norms presents a substantial challenge. The objective of this study is to investigate the translatability of cultural references in Arabic literary texts; it describes the presence of textual and matricial operational norms portrayed in the translator’s translation procedures and shifts. The analysis focuses on one Arabic novel, Basma Abdel Aziz’s The Queue, and follows Toury’s (2012) theory on translation norms. After a comprehensive examination of "The Queue," data were methodically underscored, classified, and analysed. The findings emphasize the translator's key role in addressing cultural disparities between Arabic and English societies through selected translation methods, ensuring adherence to the target language's norms despite significant linguistic and cultural gaps. According to this study, the acceptability of the target text is enhanced not only because of cultural requirements but also because of language norms, which prevail over translation choices that can be applied by the translator.

An Analysis of the Problems Encountering Libyan Pre-service Translators in the Faculty of Arts, the University of Benghazi, in Translating Arabic Texts into English

This paper aims to explore the deficiencies that translation students encounter when transferring texts from their L1, Arabic, into English, their L2. They make mistakes when they translate in terms of syntactic, non-equivalent lexical, and synonymy errors. Another aim of this study is to review the literature focusing on the interference of the translator’s L1 in the translation process and therefore its impact on the translation outputs. In order to investigate this interference, we have collected five Arabic sentences from the internet. Ten randomly students of English were chosen as a sample of this study from Semester four at the University of Benghazi (El-Marj Campus). They were asked to translate these sentences into English without consulting other people. Only Arabic-English dictionaries were allowed in order to see how they could deal with them. The results of this investigation showed that these students failed to provide good translations of these sentences due to the interference of their mother language, Arabic, to the translation process.

The Gaza-Israel War Terminology: Implications for Translation Pedagogy

Student translators at my college take a Media Translation course in which they translate the latest news stories. This study proposes a model for integrating Gaza-Israeli war terminology and texts in translation instruction to familiarize the students with such terminology such as (names of weapons (grenades, mortar, drones, missiles, Merkava, Cornet anti-armor, mortar shells), toponyms (Khan Younis, Maghazi, Sderot, Ashkelon), crossings (Rafah, Erez), Jihadist groups and brigades (Islamic Jihad, Golani), military actions (incursion, bombing, shelling, genocide, displacement) war metaphors (target bank, carpet bombing, scorched earth, fire belt, Philadelphia Axis, Hannibal's plan), (UNRWA, Gaza hospitals, starvation, humanitarian aid) and others. English and Arabic texts can be collected from mainstream media as RT, BBC, CNN, Al-Jazeera and Al-Ghad. A class blog can be created for posting translations, corrections, discussions, and feedback. The students can practice full, summary, and conceptual translation and avoid word-for-word translation. They can watch news stories about the Gaza-Israel war, write a summary translation of it and receive comments and feedback. Beginners can translate short news excerpts (few lines). Students make sure their translations are cohesive, make sense and are easy to read. Students should use Google Translate and artificial intelligence (AI) with caution and should read the same news story in both English and Arabic to get used to the terminology and their equivalents. The instructor serves as a facilitator. Further instructional guidelines and recommendations are given.

Melaka in the Arabic, Persian and Turkish sources

ABSTRACT This paper examines Melaka’s links with the Middle East during the long fifteenth century through the evidence of the sources written in Arabic, Persian and Turkish. These comprise Arabic navigational texts, Arabic inscriptions from Melaka itself, Persian geographical texts, and Turkish chronicles and administrative documents. Together, they show how the Melaka sultanate was perceived and remembered in the broader Islamic world.

An analysis of customer perception using lexicon-based sentiment analysis of Arabic Texts framework

Sentiment Analysis (SA) employing Natural Language Processing (NLP) is pivotal in determining the positivity and negativity of customer feedback. Although significant research in SA is focused on English texts, there is a growing demand for SA in other widely spoken languages, such as Arabic. This is predominantly due to the global reach of social media which enables users to express opinions on products in any language and, in turn, necessitates a thorough understanding of customers’ perceptions of new products based on social media conversations. However, the current research studies demonstrate inadequacies in furnishing text analysis for comprehending the perceptions of Arabic customers towards coffee and coffee products. Therefore, this study proposes a comprehensive Lexicon-based Sentiment Analysis on Arabic Texts (LSAnArTe) framework applied to social media data, to understand customer perceptions of coffee, a widely consumed product in the Arabic-speaking world. The LSAnArTe Framework incorporates the existing AraSenTi dictionary, an Arabic database of sentiment scores for Arabic words, and lemmatizes unknown words using the Qalasadi open platform. It classifies each word as positive, negative or neutral before conducting sentence-level sentiment classification. Data collected from X (formerly known as Twitter, resulted in a cleaned dataset of 10,769 tweets, is used to validate the proposed framework, which is then compared with Amazon Comprehend. The dataset was annotated manually to ensure maximum accuracy and reliability in validating the proposed LSAnArTe Framework. The results revealed that the proposed LSAnArTe Framework, with an accuracy score of 93.79 %, outperformed the Amazon Comprehend tool, which had an accuracy of 51.90 %.

Islamic marketing of conventional banks: bridging managers’ and clients’ perceived gaps

Purpose While the Noble Quran dictates the prohibition of interest, conventional banks promote Islamic banking by opening Islamic banking windows. Against this backdrop, this study aims to investigate the perceived gaps between managers and clients in Islamic marketing and banking, focusing on conventional banks’ Islamic banking windows. Design/methodology/approach Guided by a qualitative approach, semi-structured personal interviews and observations served as the data collection methods, involving 25 banks and 50 respondents in 3 different districts, namely, Shirajganj, Rajshahi and Chapainawabganj of Bangladesh from January to October 2023. The data were analysed using ATLAS.ti 2023 to explore codes and quotations derived from 14 interview questions. Further, ATLAS.ti 2023 facilitated synthesizing content, concepts, code occurrence, network analysis and thematic analysis. Findings Islamic and non-Islamic banks use Quranic verses, hadiths (prophetic traditions), images of mosques, the Kaaba and Arabic texts as Islamic marketing tools. These spiritual, divine and prescriptive tools are associated with Islamic banking. However, conventional banks receive criticism for having separate Islamic banking windows to serve religiously conscious clients, which generates tension among clients and bank managers. Practical implications The findings can theoretically assist academics in examining conventional banks’ Islamic marketing and banking practices, opening Islamic banking windows. Importantly, Shariah boards can play policy roles in safeguarding the function of Islamic marketing and banking. Managers can use the findings to anticipate client perceptions and enhance Islamic marketing and banking strategies. Likewise, the social implications include the explicit stance of Shariah to mitigate the mixture of halal and haram banking. Originality/value This pioneering study explores the perspectives of Islamic banking windows by non-Islamic banks. The combination of Islamic marketing and banking is a noteworthy novelty in this study and deserves recognition for its unique contribution to halal marketing and finance.

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Abstract: Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

Karakteristik Manuskrip Al-Qur`an Koleksi Kesultanan Kutaringin Kotawaringin Barat Kalimantan Tengah

This research aims to uncover the characteristics of the Al-Qur'an manuscripts in the collection of the Sultanate of Kutaringin at the Mangkubumi Palace, with a specific focus on the textual aspect. The characteristics of the mushaf will be identified through the analysis of the verse texts and other related aspects based on the theories within ‘Ulūmul Qur`ān (the sciences of the Qur`an). This research combining a literature-based and a field-based methods. The combination of these methods is necessary when the data is not sufficiently representative to draw conclusive findings. The research design is qualitative, aiming to obtain a comprehensive understanding of this particular aspect. The primary data source of this research is the seventh volume of the mushaf in Mangkubumi Palace and other related secondary data sources. The results of this research indicate that the Qur’an manuscript of Kutaringin Sultanate in Mangkubumi Palace, especially the seventh mushaf have identical characteristics to the style of Ottoman Turkish manuscripts. This showed on the writing of some diacritical mark aspects (ḍabṭ), the markings for dividing of the Qur’an (ḥizb), the calligraphy and the mushaf illumination that clearly using Turkish writing style. This mushaf, written in the early 19th century, was transcribed using a mixed rasm (written form or script of an Arabic calligraphy text) style, namely the rasm ‘uṡmānī and the rasm imlā`ī, and consistently written with the qirā`āt ‘Āṣim riwayat Ḥafṣ.

Neither Corpus Nor Edition: Building a Pipeline to Make Data Analysis Possible on Medieval Arabic Commentary Traditions

We have built a suite of tools in Python to proficiently analyze text reuse and intertextuality for a specific kind of set of medieval Arabic texts (commentaries) available in print. We take these printed editions, scan them, pre-process the images, give it to an OCR engine, clean the results, and store it in a data structure that mimics the explicit intertextual relation the texts have, and continue to perform data analysis on it. Digital approaches to medieval Arabic texts have either been at the micro-level in what has become known as a ‘digital edition’, i.e. the digital representation of one text, densely annotated, most commonly in TEI-XML, or it has been done at the macro-level in what is called a ‘digital corpus’, consisting of thousands of loosely encoded and sparsely annotated plain text files, accompanied by an entire infrastructure and high-performing software to perform broadly scoped queries. The micro-level generally is at the level of tens of thousands of words while the macro-level can be at the level of over a billion words. The micro-level is explicitly designed to be human readable first, while the macro-level is built to be machine readable first. At the micro-level, every little detail needs to be correct and in order, while at the macro-level a fairly large margin of error is still negligible as a mere rounding error. Amidst these levels we have been seeking a meso-level of digital analysis: neither edition nor corpus, but rather a group of texts at the level of hundreds of thousands to millions of words, with a small but perceptible margin of error, and a light but noticeable level of annotations, principally geared towards machine readability, but with ample opportunity for visual inspection and manual correction. In this paper we explain the rationale for our approach, the technical achievements it has led us to, and the results we so far obtained.

Exploring Gender Differences in Fatwa through Machine Learning

This paper focuses on exploring the differences in inquiries made by men and women within a religious context. Additionally, we aim to ascertain whether it’s feasible to forecast the popularity of answers and the factors contributing to their popularity. To achieve this, we compile a new dataset comprising 40,000 question-answer pairs categorized by gender and popularity. These are sourced from online question-and-answer platforms. Our methodology involves comprehensive experimental analysis, utilizing advanced Arabic text preprocessing alongside machine learning algorithms. We concentrate on two primary objectives: predicting the gender of the questioner and forecasting the popularity of answers. Furthermore, we delve into thematic variations based on gender and address pivotal research queries that offer new perspectives within this domain. These include investigating the differences between questions posed by women versus men, exploring the potential for automated classification of queries by gender, predicting the popularity of fatwas, and identifying the contributing factors to their popularity. Our experimental findings demonstrate a 98% accuracy in gender prediction, precise predictions of popularity with minimal margin for error, and the identification of topics and their associations that are more inclined towards either men or women. We intend to share both the dataset and the source code openly with the research community.

Efektivitas Artificial Intelligence Text to Speech dalam Meningkatkan Keterampilan Membaca

As a second international language, Arabic has an existence to be learned by the general public, especially in formal and non-formal educational institutions. The use of Artificial Intelligence (AI) technology in various aspects of life has brought significant impacts, including in the field of education and learning. One increasingly popular application of AI is Text-to-Speech (TTS), which allows computers to convert written text into audible voice. This study aims to help users understand Arabic text reading, especially for users who cannot read Arabic script. And can be one of the tools or alternatives in the process of learning Arabic to improve reading skills. AI text-to-speech (TTS) can be very useful in improving reading skills. One way that AI TTS can help is in Clear Pronunciation: AI TTS can produce clear and accurate sounds, helping listeners understand words correctly and mimic proper pronunciation.

Toury’s Norms as Applied to the Translation of Arabic Literary Texts Into English

This study investigates the applicability of Toury’s norms to the translation of Arabic literary texts into English. It also tackles the translation of sociocultural terms from Arabic literary texts into English and highlights the sociocultural differences between the two languages. Moreover, it uses Toury’s norms to analyze the translated selections of texts by two translators to determine whether each decided to be loyal to the target reader and explain the expression, or if they decided to be loyal to the source text, translate it, and then explain the influence of each decision on the other norms. For this purpose, the researchers selected 40 excerpts from the novel Midaq Alley (1960) by 1988 Nobel Prize winning author (and the “Father of Arabic Literature”), Naguib Mahfouz, along with two translations by Trevor LeGassick (1966) and Humphrey Davies (2011). These 40 texts contained 74 cases or expressions concerning religion, clothing, food, tools, places, and binomials. The study concluded that Toury’s norms (that is, initial norms, preliminary norms, and operational norms) do, indeed, apply to the translation of Arabic literary texts into English. The study shows that the norms are applicable and that the English-translated texts from the Arabic language can be assessed by using these norms.

Challenges Related to Grammatical and Morphological Processing of Arabic Texts by Means of Artificial Intelligence

This research aims to study the challenges facing artificial intelligence in processing Arabic texts grammatically and morphologically, by conducting a survey on a random sample of Microsoft Word 2010 users. The research provided a theoretical framework for the subject of the study, as it addressed the concept of artificial intelligence, as well as the general challenges facing artificial intelligence and some challenges specific to processing Arabic texts. The research relied on the descriptive approach, and this helped highlight the importance of the study and provide a comprehensive vision of its subject. The study concluded a set of results, the most important of which are: - There are many challenges facing users in processing Arabic texts grammatically and morphologically, such as derivation, morphology, and the breadth and comprehensiveness of the Arabic language. - The inability of artificial intelligence so far to reach a high degree of quality and efficiency in processing Arabic texts grammatically and morphologically. - The possibility of processing the Arabic language in the future by adding some software operations that help develop the machine’s capabilities to understand the Arabic language and the ability to express it effectively, through the use of new artificial intelligence methods, such as deep learning and machine learning. The study recommended a number of important recommendations, which are: - Microsoft Word is constantly updated by Microsoft, in order to ensure the quality and efficiency of the program in processing Arabic texts grammatically and morphologically. - Encouraging students and researchers to conduct more scientific research in the field of automated processing of linguistic texts, in order to enhance the progress and development of this vital field.

TAHLILUL AKHTHA’ FI MAHAROTI AL-KALAM BI AL-LUGHAH AL-ARABIYAH LITHULLABI MA’HADI SIRAJU AL-THALIBIN AL-ISLAMI

This research is motivated by the issues related to the low ability of students in pronouncing letters, words, or sentences in Arabic, especially for beginner learners, to prevent future errors that may hinder the learning process. This study aims to analyze students' errors in reading Arabic texts from a phonological perspective. It is a qualitative research with a descriptive research design. The results of this research are as follows. Firstly, the phonological error forms in Arabic speaking skills among students of the Siraaj Al-Thalibin Islamic boarding school include errors in throat consonant groups (?????) and tongue groups (??????); vowel errors such as vowel deletion, vowel substitution, and long-short vowel errors; diphthong errors; double consonant errors (????); and assimilation errors (??). Secondly, the factors causing errors in Arabic language speaking skills include linguistic and non-linguistic factors. Non-linguistic factors such as low interest and motivation in language learning, and lack of application of the Arabic language.

From the Frame Tale to Framing Intertextuality

The application of the term framing to Arabic texts is commonly confined to the frame tale type of which the Arabic tradition produced one of the most popular works, The Thousand and One Nights. This issue transcends the study of the Nights and the associated story-within-a-story trope, moving toward a recon­stitution of the study of the frame tale as part of literary framings more broadly conceived, which include multiple notions and applicabilities. In so doing, “Framing Narratives” aims to fill in a lacuna both in the study of frame tales in the Arabic and other Middle Eastern traditions such as Kalīla and Dimna, Barlaam and Josaphat and the Seven Sages in their different versions and the broadening of the framing category itself. This introduction presents a comprehensive and cumulative definition of framing narratives including notions of mise en abyme, mise en reflet/mise en série, paratext, and parergon, among others. The overall issue will contain definitory and structural-functionalist attempts, with additional foci on the translingual variation of frame narratives, and the intertextual variation of frames, connecting the realms of adab and poetry, philosophical and anthological genres, and scripture. Key words: Framing, frame narrative, Arabic literature, Kalīla wa-Dimna, The Thousand and One Nights, allegory of the man in the well, Gérard Genette, Werner Wolf, contextualist narratology

Analisis Latihan Soal Bahasa Arab pada Buku Ajar Mahir fi Lughoh Arabiyah Perspektif Taksonomi Bloom

This study aims to analyze the practice questions contained in the textbook "Mahir fi Lughoh Arabiyah" using Bloom's taxonomic approach. This approach is used to evaluate the level of thinking and understanding of students tested through practice questions. The analysis method is carried out by categorizing the problems in the textbook into six levels of Bloom's taxonomy, namely knowledge, understanding, application, analysis, synthesis, and evaluation. The results of the analysis show that the textbook "Mahir fi Lughoh Arabiyah" has successfully implemented Bloom's taxonomic approach well. The practice questions in the textbook cover different levels of taxonomy, from basic knowledge of Arabic vocabulary and grammar to students' ability to apply and analyze authentic Arabic texts. The study concluded that the textbook "Mahir fi Lughoh Arabiyah" had adopted Bloom's taxonomic approach well in designing practice problems. By covering various levels of taxonomy, this textbook is able to encourage students' thinking and understanding in learning Arabic. The recommendation of this study is the importance of using Bloom's taxonomy in designing practice problems to improve the effectiveness of Arabic learning in higher education.

Шейх ал-ислам Закариййа ал-Ансари в книге «Вафиййат ал-аслаф» Ш. Марджани

The article is a study of a fragment from the Arabic manuscript source “Wafiyyat al-aslaf wa tahiy-yat al-akhlaf” from the heritage of a Tatar theologian and 19th-century historian Shihab al-Din al-Marjani (Shigabutdin Marjani). The fragment is dedicated to the biography of a Shafi’i faqih Zakar-iyya al-Ansari (d. 926/1520), the supreme judge of Egypt of the Mamluk era, awarded the honorary title “Shaykh al-Islam”. Based on the manuscript from the collection of the academic library of Ka-zan Federal University, the Arabic text of al-Ansari’s biography was restored in modern orthography and then translated into Russian with comments and a brief analysis in the historical and theological context. Tatar theologian Shihab al-Din al-Marjani provides the basic information about the life and contribution of Sheikh al-Islam Zakariyya al-Ansari to Islamic sciences and emphasizes that he was one of the renovators of the Islamic faith (mujaddid) of his era. The authors of the article, on the one hand, tried to fill a gap in domestic Islamic studies regarding information about the outstanding Shafi’i jurist Zakariyya al-Ansari; on the other hand, they pointed out the importance of studying and translating a unique domestic manuscript source on the history of Islam – the biographical dic-tionary “Wafiyyat al-aslaf wa tahiyyat al-akhlaf” by Shihab al-Din al-Marjani, which has not yet been published or translated in full. For the first time, a fragment from this source with translation is introduced into scholarly discourse.

Extractive Arabic Text Summarization Using PageRank and Word Embedding

Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and PageRank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%.

Data Representation for Deep Learning - Based Arabic Text Summarization Performance Using Python Results

A sequence-to-sequence model is used as the foundation for a suggested abstractive Arabic text summarizing system. Our goal is to create a sequence-to-sequence model by utilizing multiple deep artificial neural networks and determining which one performs the best. The encoder and decoder have been developed using several layers of recurrent neural networks, gated recurrent units, recursive neural networks, convolutional neural networks, long short-term memory, and bidirectional long short-term memory. We are re-implementing the fundamental summarization model in this study, which uses the sequence-to-sequence framework. Using a Google Colab Jupiter notebook that runs smoothly, we have constructed these models using the Keras library. The results further demonstrate that one of the key techniques that has led to breakthrough performance with deep neural networks is the use of Gensim for word embeddings over other text representations by abstractive summarization models, along with FastText, a library for efficient learning of word representations and sentence classification.