Arabic Text Research Articles

Dotless Arabic Text for Natural Language Processing

Abstract This article introduces a novel representation of Arabic text as an alternative approach for Arabic NLP, inspired by the dotless script of ancient Arabic. We explored this representation through extensive analysis on various text corpora, differing in size and domain, and tokenized using multiple tokenization techniques. Furthermore, we examined the information density of this representation and compared it with the standard dotted Arabic text using text entropy analysis. Utilizing parallel corpora, we also drew comparisons between Arabic and English text analysis to gain additional insights. Our investigation extended to various upstream and downstream NLP tasks, including language modeling, text classification, sequence labeling, and machine translation, examining the implications of both the representations. Specifically, we performed seven different downstream tasks using various tokenization schemes comparing the standard dotted text with dotless Arabic text representations. Performance using both the representations were comparable across different tokenizations. However, dotless representation achieves these results with significant reduction in vocabulary sizes, and in some scenarios showing reduction of up to 50%. Additionally, we present a system that restores dots to the dotless Arabic text. This system is useful for tasks that require Arabic texts as output.

Testimony, Lying and Calumny at the Interface of Islamic Law and Arabic Poetry

Abstract The significance of the concept of ‘testimony’ permeates Islamic jurisprudence and it is invoked in Arabic literary texts as well. Truthfulness and lying (kaḏib) were a preoccupation of many Arabic literary critics and authors in assessing literary works, while the notion of ‘testimony’ could be used figuratively in poetic texts. On the other hand, Islamic law emphasises the crucial role of trustworthy testimony in legal proceedings and has developed a sophisticated system of ascertaining the integrity of witnesses. Within this context Muslim jurists sometimes explicitly discuss poets and poetry, not surprisingly in view of the fact that the Quran characterises poets as mendacious, and some ḥadīṯs also denounce poetry. Certain literary genres in particular (especially hiğāʾ and ġazal) could be interpreted by jurists as cases of calumny that substantiate legal charges of false testimony (qaḏf). Offering a copious source of intertextual references at the interface of law and literature, legal discussions of the problem of potential qaḏf in poetry are the focus of this paper and can help clarify the peculiar ways in which premodern Muslim jurists would view and assess literary texts.

Impact of Machine Learning Integration in Qur’anic Studies

The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur’anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur’anic studies. This work explores the convergence of machine learning methodologies and Qur’anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur’anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur’anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur’anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.

Various Problems And Solutions In Translating Between Arabic And Indonesian

This research examines the challenges and strategies involved in translating Arabic texts, particularly Quranic verses, into Indonesian. Given the significant linguistic and cultural differences between Arabic and Indonesian, translation efforts face numerous obstacles, including issues related to morphology, syntax, semantics, and cultural context. These challenges are compounded by the complexity of translating polysemous words, synonyms, idioms, and metaphors. This research employs qualitative methods, focusing on the translation processes, and offers solutions grounded in established translation techniques. By analyzing selected Quranic verses and creatively crafted Indonesian examples, the research highlights the importance of maintaining grammatical integrity, adapting idioms appropriately, and addressing untranslatability and collocations to ensure that translations are accurate and culturally relevant.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

The Leiden Manuscript of the Hundred and One Nights

Abstract This paper investigates the Arabic manuscript of the Hundred and One Nights (Miʾat laylah wa-laylah) registered at Leiden University, the Netherlands. First, I prove that the manuscript was once owned by René Basset (1855–1924) by explaining its characteristics and comparing it with references in the French translation of Miʾat laylah wa-laylah by Maurice Gaudefroy-Demombynes (1862–1957). Second, I discuss how the Leiden manuscript is situated in comparison to the other seven texts of the Hundred and One Nights, focusing on the order of the embedded stories and the frame story. The Arabic text of the frame story in the Leiden manuscript is provided at the end. The Leiden manuscript exhibits similar characteristics to some of the other manuscripts of the Hundred and One Nights, while it also contains its own special features.

APPLIED LINGUISTICS WITH DEEP LEARNING-BASED DATA-DRIVEN TEXT-TO-SPEECH SYNTHESIZER FOR ARABIC CORPUS

The field of Applied Linguistics, which deals with language and its practical uses, connects with technology in interesting methods, especially in the advancement of text-to-speech (TTS) synthesizers. TTS synthesizers change written text into spoken words, deploying ethics from phonology, phonetics, and syntax to create natural-sounding speech. Within linguistics use, these methods are invaluable purposes such as increasing communication in fractal human–computer interactions (HCIs), language-learning tools, and providing accessibility solutions for visually impaired individuals. TTS purposes at synthesizing understandable and natural speech from text, and it has advanced quickly in recent times because of the progress of artificial intelligence (AI). During past years, deep learning (DL)-based TTS techniques have been established quickly, enabling the generation of natural speech with a high-quality narrator that matches human levels. Creating TTS methods at the quality of the human level has always been the aspiration of speech synthesis practitioners. Although current TTS techniques achieve impressive voice quality, there remains an evident gap in quality compared to human recordings. In this paper, we present an Applied Linguistics with Deep Learning-based Data-Driven Text-to-Speech Synthesizer (ALDL-DDTTS) technique for Arabic corpus. The ALDL-DDTTS technique mainly aims to detect the text and convert it into speech signals on Arabic corpora. In the ALDL-DDTTS technique, a multi-head attention bi-directional long short-term memory (MHA-BiLSTM) approach can be employed with fractal optimization methods to predict the diacritic and gemination signs. Additionally, the Buckwalter code has been deployed for capturing, storing, and displaying the Arabic texts. To boost the efficiency of the ALDL-DDTTS technique, the hyperparameter selection process uses the fractal ant lion optimization (ALO) algorithm. For examining the boost performance of the ALDL-DDTTS methodology, a wide range of simulations is involved. The experimental outcomes illustrated that the ALDL-DDTTS technique reaches better performance than other models.

The Adoption of Artificial Intelligence Technologies in Arab Newsrooms: Potentials and Challenges

Arab newsrooms and journalists are still attempting to process how to benefit from using artificial intelligence (AI) in their daily news production. Many have started experimenting with generative AI. However, in countries with strong economies, or in newsrooms funded by strong economies like in the UAE, Saudi Arabia, and Qatar, news organizations have endorsed AI technologies beyond ChatGPT and similar tools. The majority of Arab journalists, however, are yet to understand what is meant by AI. Very few news organizations in the region have strategically approached the question of AI. Arab newsrooms need to examine when and why they need to use AI technologies, whether they have the infrastructure or can afford the resources. Implementing AI in Arab newsrooms presents an additional set of challenges, including language complexities, cultural sensitivities, potential biases in algorithms, and the need for tailored solutions that resonate with local audiences. The ethical implications present its own set of challenges. ChatGPT has proven to generate inaccurate data in Arabic. Resources are to be put in place to ensure the model is trained on a diverse and comprehensive dataset of Arabic text. Implementing bias detection and mitigation techniques to ensure the models are free from biases and offensive content is crucial. Newsrooms operating in authoritarian regimes have to be aware of governments' attempts to use generative AI to whitewash and promote state-centered actions and policies. Maintaining editorial integrity remains an important consideration.

The Verbal Root <i>Raqa‛</i> in Moslem and Biblical-Aramaic Texts

The paper pursues two goals: (1) to trace the evolution of semantics of the verbal root raqa‛ and its lexical derivatives (participles, verbal names, etc.) in Persian and Arabic Moslem texts; and (2) to compare the obtained results with semantics of the same Semitic root in the Biblical-Aramaic texts of the Old Testament. The comparison reveals a great variety in branches of the root raqa‛ in Persian and Arabic Moslem texts and shows a shortage of those in the Biblical-Aramaic texts. The author explains this phenomenon by the specific contents of the Old Testament where the verbal root raqa‛ with its derivatives is used to glorify God the Creator of the Universe and to curse idolatry.

Results of the study of Tatar epigraphic monuments of the Vysokogorsky district of the Republic of Tatarstan

The article is devoted to the study of Tatar epigraphic monuments preserved in the Vysokogorsky district of the Republic of Tatarstan. As a valuable source, they play an important role in the study of the history, culture and language of the Tatar people, since they contain information that cannot be obtained from other historical sources. During an epigraphic expedition in this area, the author for the first time identified over 1200 monuments dating from the 13th century to 1939. The article presents the results of this work, describes the most significant and interesting examples of epigraphic monuments of scientific interest. The novelty of the study lies in the fact that some examples of this epigraphic heritage are introduced into scientific circulation for the first time, and also presents the results of the study of Arabic and Tatar texts on stones. For citation: Gaynutdinov A.M. Results of the study of Tatar epigraphic monuments of the Vysokogorsky district of the Republic of Tatarstan. From History and Culture of Peoples of the Middle Volga Region. 2024, vol.14, no.3, pp.137–145. https://doi.org/10.22378/2410-0765.2024-14-3.137-145 (In Russian)

Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text

Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text

Semantic Study Of The Arabic Text The Quran Verses: Educational Implications Regarding Individual Responsibility And Its Application

This study aimed to derive educational contents from the verses on individual responsibility mentioned in the Qur’an, and from this primary goal emerge the following sub-goals: deducing educational principles from the verses on individual responsibility, explaining the educational values deduced from the verses on individual responsibility and highlighting contemporary educational applications deduced from the verses on individual responsibility, which reached ten verses. The study relied on the descriptive approach—the deductive analytical method in extracting educational contents from the specific verses. The study results indicated that the Holy Qur’an, including its verses on individual responsibility, includes many educational contents of principles and values, which we must work to derive to be applied in the educational field. The study clarified the principles of individual responsibility that should be developed in young people, the most important of which are justice, self-control, bearing responsibility, self-struggle, and decision-making. The study also resulted in values derived from the verses of individual responsibility, the most important of which are fear of Allāh, warning against injustice to the soul, spending, purifying the soul, gratitude, good deeds, and modesty. The study highlighted the educational importance of each principle and value mentioned in the noble verses and that each value significantly impacts human life. The study also presented some contemporary educational applications that can be exploited and applied in developing individual responsibility through the family and school.

Customers' sentiment on food delivery services: An Arabic text mining approach

The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual Information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches.

Identification of Tampering in Arabic Text Documents Using Zero-Watermarking Algorithm

The internet makes sharing information easy, but protecting copyrights and verifying the authentication of online text remains a challenge. Current methods for securing Arabic text are limited. This research introduces a new tool: a zero watermark algorithm designed specifically for Arabic text. This new algorithm protects Arabic text by analyzing its content. It creates a watermark based on a chosen keyword (at least 6 characters, no duplicates), and how often each character appears in different forms (separate, connected), and positions (beginning, middle, and end) within the text. The proposed method was tested on nine different Arabic text samples of varying size, simulating various tampering attempts like inserting, deleting, and rephrasing text. The results are impressive, showing that the algorithm outperforms existing methods in detecting even minor changes.

Aḥmad al-Ghazālī’s al-Tajrīd fī Kalimat al-Tawḥīd: “A Primer on the Statement of Tawḥīd”

Abstract This article presents an introduction to and the first complete English translation of Aḥmad al-Ghazālī’s (d. 517/1123 or 520/1126), al-Tajrīd fī kalimat al-tawḥīd, his most widely received Arabic text. The title has a double entendre, meaning both “A Primer on the Statement of Tawḥīd,” and “Disengaging Through the Statement of Tawḥīd.” It is designed to serve both purposes. The first half provides a spiritual typography, juxtaposing “the people of grace” (ahl al-faḍl) to “the people of justice” (ahl al-ʿadl). The former are those who maintain their covenant with God and follow the Prophet Muhammad. The latter are those who break the covenant and follow Iblīs. The second half guides aspirants through the levels of spiritual development and the corresponding modes of remembrance (dhikr), detailing three modes of dhikr – lā ilāha illa Llāh (No god, but God), Allāh, and huwa huwa (He, He). These correspond to three levels within the human being, the heart (al-qalb), the spirit (al-rūḥ), and the secret (al-sirr). The goal is to provide an overview of the means whereby one can disengage from the blameworthy inclinations of their soul through the progressive levels of dhikr until the secret predominates over the spirit and the spirit predominates over the heart, such that one is able to focus solely upon God.

On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy.

The Educational Context In The Literature Of Ibn Al-Muqaffa Through His Book Al-Adab Al-Saghir/ السياق التربوي في أدب ابْنِ الْمُقَفَّع من خلال كتابه الْأدب الصغير

The purpose of this study is to analyze the contents of the book "Al-Adab Al-Shaghir" by the author Abdullah Ibn Al-Muqaffa, and identify the educational context of the texts of this book. This study uses an inductive approach to extrapolate Arabic texts and extract research materials and descriptive-analytical (content analysis) To analyze. The author focuses on social behavior and discusses topics including the importance of self-discipline and self-control and the importance of friendship and positive social relationships. It also emphasizes ethical behavior and the importance of moral values, personal relationships, and social responsibility. He discusses educational issues with reason, wisdom, and literature, which increases the influence of his philosophy on readers and contributes to establishing educational values in society. The study results also show that the content and values of education, the most prominent of which are the love of knowledge, choosing good friends, Honesty and integrity, and their role in encouraging personal and social growth, Self-awareness, and self-control. This book contains many rhetorical methods with the educational implication that a wise person must be at odds with himself and judge himself. Among the most prominent educational means in Ibn al-Muqaffa's texts are education by example, the need for ethics and discipline (education) and its role in the context of watering the mind, and developing society to benefit from these contents in raising children in the current era, and it is recommended that these values need to be included in educational books so that pre-university students can contribute to instilling values and purifying the minds of the younger generation.

Machine Learning Approach for Arabic Handwritten Recognition

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

Towards Improving E-Commerce Customer Review Analysis for Arabic Language Opinion Mining

In recent years, the rapid development of Internet-related technologies has facilitated the widespread adoption of online purchasing as a convenient means of satisfying consumer needs. Conducting sentiment analysis on user reviews on e-commerce platforms can substantially improve customer satisfaction. In order to resolve this issue, we propose a novel model for sentiment analysis that employs hybrid deep learning ensembles, combining RNN and TreeLSTM with AraBERT as the word embedding. Our research concentrates on creating a hybrid deep-learning model to predict Arabic sentiment accurately. We employ deep learning models with various word embeddings, such as RNN, LSTM, BiLSTM, TreeLSTM, RNN-LSTM, RNN-BiLSTM, and RNN-TreeLSTM. Multiple open-access datasets are used to evaluate the efficacy of the proposed model, including the BRAD dataset, the ARD dataset, and merged datasets containing 610,600 items. The experimental findings indicate that our proposed model is well-suited for evaluating the sentiments expressed in Arabic texts. Our strategy starts with extracting features using the Arabert model, followed by developing and training five hybrid deep-learning models. We attained a significant accuracy improvement of 0.9409 when comparing our method to traditional and hybrid deep learning techniques. This demonstrates that our proposed model precisely analyzes sentiment. These findings are important for enhancing the comprehension of emotions conveyed in Arabic text and have practical implications for various applications, especially e-commerce. By accurately assessing sentiment, businesses can better comprehend customer preferences and enhance consumer satisfaction by enhancing their offerings.

Ta'lîm Kitāb Nubdzah Al-Bayān Li Tarqiyah Mahārah Al Qiraāh Al ‘Arabiyah

<p style="text-align: justify;" dir="ltr"><span> </span><span>Arabic is an important part of the learning curriculum so that Arabic is made the official language at educational foundations, as well as at the Al-Amien Tegal Prenduan Islamic Boarding School. At this Islamic boarding school, the principles of learning Arabic are applied through the book Nubdzatul Bayan which consists of five volumes and a supplement. The aim of this research is to learn the rules of the Arabic language using the Nubdzatul Bayan book in order to improve the reading skills of Arabic texts for beginner students. The research method used is qualitative, with data collection techniques in the form of interviews, observation and documentation, with the research object being the students. The research results show that the strategy for learning Arabic rules using the Nubdzatul Bayan book is carried out through deductive methods and question and answer methods, where the teacher plays an important role in the learning process. First, the teacher writes the rules of Nahwu and Sharaf on the board along with examples of numbers in Arabic, Face-to-face learning is limited to only 3 hours per day, while learning evaluation includes daily, weekly, monthly and annual evaluations carried out using written tests and oral tests. Thus, this research provides insight into the effectiveness of using the Nubdzatul Bayan book in teaching Arabic language rules and improving Arabic text reading skills for beginner students at the Al-Amien Tegal Prenduan Islamic Boarding School.</span></p>