The Arabic language presents unique modeling challenges, such as its morphological complexity, orthographic ambiguity, dialectal variations, and orthographic noise. Furthermore, the scarcity of linguistic resources dedicated to Arabic and the limited availability of detection tools compound the difficulties in effectively identifying and mitigating offensive language in Arabic text. Given Arabic's linguistic richness and diversity, it is essential to develop robust and accurate methods for detecting and addressing offensive language to ensure the safety and well-being of Arabic-speaking online communities. This study aims to enhance the performance of Arabic offensive tweet classification by proposing a novel framework combining advanced preprocessing techniques and state-of-the-art classification models in an ensemble methodology. It comprises two primary modules: preprocessing module and classification module. The preprocessing module incorporates AraBERT preprocessing, emoji-to-word interpretation, and punctuation removal. The classification module encompasses neural networks (NN), LSTM, and AraBERT. The proposed ensemble model combines a fine-tuned AraBERT with two NN layers, LSTM, ReLU, and Sigmoid activation functions. The model achieved state-of-the-art performance, surpassing all other approaches in terms of Accuracy and F1-score. These results highlight the effectiveness and potential of leveraging advanced models like AraBERT in detecting and classifying offensive language in Arabic text.

The Metaverse, an expansive online 3D virtual realm, has transformed how people work, socialize, and engage, but its growth brings critical cybersecurity challenges, particularly in data security and privacy. Since many cybersecurity incidents stem from human behavior, understanding factors influencing users’ cybersecurity practices in the Metaverse is vital. This study addresses the gap by proposing an integrated model based on Protection Motivation Theory (PMT), Health Belief Model (HBM), and Theory of Interpersonal Behavior (TIB), with trust as an additional construct. Using a hybrid structural equation modeling-artificial neural network (SEM-ANN) approach, data from 531 Metaverse users were analyzed. The results indicated that perceived vulnerability, self-efficacy, cues to action, habit, and trust significantly affect cybersecurity behavior, with “cues to action” identified as the most critical factor (97.8% normalized importance). Conversely, perceived severity, response efficacy, response costs, and facilitating conditions showed no significant impact. The findings offer theoretical insights and practical implications for Metaverse stakeholders and cybersecurity strategies.

The metaverse, a virtual immersive space, has the potential to revolutionize shopping experiences. By converging digital and physical realities, the metaverse creates an interconnected network of 3D environments that simulate real-world experiences. This presents lucrative Virtual Commerce (v-commerce) opportunities, enabling businesses to engage consumers and enhance satisfaction. Consequently, selecting the optimal v-commerce solution is critical for organizations aiming to achieve success and maximize customer engagement. Researchers have explored various strategies to address the unique challenges and opportunities in developing robust v-commerce solutions, emphasizing their potential to optimize user experiences and engagement in this evolving digital environment. By evaluating distinct design attributes, prioritizing them effectively, and managing variability in user data, this modeling approach aligns closely with the principles of Multi-Criteria Decision Making (MCDM). The primary objectives of this review paper are threefold. First, it aims to identify gaps, challenges, and unresolved issues in the existing literature on evaluating and benchmarking v-commerce applications for the metaverse. Second, it seeks to address a theoretical gap by proposing a novel decision-making framework that integrates the Ranking Alternatives based on Trace to Median Index (RATMI) and Fuzzy Weighted Zero Inconsistency (FWZIC) methods for evaluating and benchmarking v-commerce solutions. Third, it aims to summarize the common methods in the literature that are suitable for validating and evaluating the proposed framework. The results demonstrate that employing MCDM techniques provides an effective framework for evaluating metaverse v-commerce solutions. This paper offers valuable insights to decision-makers, researchers, technology developers in retail and e-commerce, and consultants seeking to leverage v-commerce solutions within the metaverse.

The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFake-detection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models’ produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of "DeepFake text detection", with a special focus on examining the boundaries of human detection.

Autonomous vehicles (AVs) offer several benefits, such as improving road safety, mitigating traffic congestion, and reducing fuel consumption and gas emissions. Despite these benefits, their adoption rate remains limited due to various factors influencing users’ decisions. While previous studies have identified numerous factors influencing AV adoption using various adoption frameworks, the factors have not been comprehensively analyzed and synthesized. Thus, this systematic review aims to bridge this gap by identifying and classifying the factors influencing the adoption of AVs. Out of 3,532 collected research papers, 71 empirical studies were analyzed thoroughly. The findings demonstrated that the technology acceptance model (TAM) was the most widely used model for investigating AV adoption. The identified factors in the analyzed studies were classified into distinct categories: psychological and behavioral factors, technological factors, social factors, environmental factors, security and privacy factors, AV-related factors, risky and negative factors, conditional factors, and monetary factors. We have proposed an AV adoption framework grounded in this taxonomy to direct subsequent empirical research. We have also highlighted numerous agendas to serve as a blueprint for future AV adoption studies. This review offers various theoretical insights and actionable recommendations for multiple AV research, development, and implementation stakeholders.

Relation extraction from unstructured Arabic text is especially challenging due to the Arabic language complex morphology and the variation in word semantics and lexical categories. The research documented in this paper presents a hybrid Semantic Knowledge base - Machine Learning (SKML) approach for extracting complex Arabic relations from unstructured Arabic documents; the proposed approach exploits the principles of Functional Discourse Grammar (FDG) to emphasise the semantic and pragmatic properties of the language and facilitate the identification of relation elements. At the initial phase, the novel FDG-SKML relation extraction approach deploys a lexical-based mechanism that utilises a purposely built domain-specific Semantic Knowledge to encode the semantic association between the identified relations’ elements. The evaluation of the initial stage evidenced improved accuracy for extracting most complex Arabic relations. The initial relation extraction mechanism was further extended by integrating its output into a Machine Learning classifier that facilitated extracting especially complex relations with significant disparity in the relation elements’ presence, order, and correlation. Using Economics as the problem domain, experimental evaluation evidenced the high accuracy of our FDG-SKML approach in complex Arabic relation extraction task and demonstrated its further improvement upon integration with machine learning classifiers.

Phishing involves malicious activity whereby phishers, in the disguise of legitimate entities, obtain illegitimate access to the victims’ personal and private information, usually through emails. Currently, phishing attacks and threats are being handled effectively through the use of the latest phishing email detection solutions. Most current phishing detection systems assume phishing attacks to be in English, though attacks in other languages are growing. In particular, Arabic is a widely used language and therefore represents a vulnerable target. However, there is a significant shortage of corpora that can be used to develop Arabic phishing detection systems. This article presents the development of a new English-Arabic parallel phishing email corpus that has been developed from the anti-phishing share task text (IWSPA-AP 2018). The email content was to be translated, and the task had been allotted to 10 volunteers who had a university background and were English and Arabic language experts. To evaluate the effectiveness of the new corpus, we develop phishing email detection models using Term Frequency–Inverse Document Frequency and Multilayer Perceptron using 1,258 emails in Arabic and English that have equal ratios of legitimate and phishing emails. The experimental findings show that the accuracy reaches 96.82% for the Arabic dataset and 94.63% for the emails in English, providing some assurance of the potential value of the parallel corpus developed.

A sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks.Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and dialects.We propose to self-train pre-trained language models in zero-and few-shot scenarios to improve performance on data-scarce varieties using only resources from data-rich ones.We demonstrate the utility of our approach in the context of Arabic sequence labeling by using a language model fine-tuned on Modern Standard Arabic (MSA) only to predict named entities (NE) and part-of-speech (POS) tags on several dialectal Arabic (DA) varieties.We show that self-training is indeed powerful, improving zero-shot MSA-to-DA transfer by as large as 10% F 1 (NER) and 2% accuracy (POS tagging).We acquire even better performance in few-shot scenarios with limited amounts of labeled data.We conduct an ablation study and show that the performance boost observed directly results from training data augmentation possible with DA examples via self-training.This opens up opportunities for developing DA models exploiting only MSA resources.Our approach can also be extended to other languages and tasks. 1

We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries.Identification and usage of synonyms play a significant role in improving the performance of information access applications.The idea is to construct a translation graph from translation pairs, then to extract and consolidate cyclic paths to form bilingual sets of synonyms.The initial evaluation of this algorithm illustrates promising results in extracting Arabic-English bilingual synonyms.In the evaluation, we first converted the synsets in the Arabic WordNet into translation pairs (i.e., losing word-sense memberships).Next, we applied our algorithm to rebuild these synsets.We compared the original and extracted synsets obtaining an F-Measure of 82.3% and 82.1% for Arabic and English synsets extraction, respectively.

Web site schema detection and data extraction from the Deep Web have been studied a lot. Although, few researches have focused on the more challenging jobs: wrapper verification or extractor generation. A wrapper verifier would check whether a new page from a site complies with the detected schema, and so the extractor will use the wrapper to get instances of the schema types. If the wrapper failed to work with the new page, a new wrapper/schema would be re-generated by calling an unsupervised wrapper induction system. In this paper, a new data extractor called GenDE is proposed. It verifies the site schema and extracts data from the Web pages using Conditional Random Fields (CRFs). The problem is solved by breaking down an observation sequence (a Web page) into simpler subsequences that will be labeled using CRF. Moreover, the system solves the problem of automatic data extraction from modern JavaScript sites in which data/schema are attached (on the client side) in a JSON format. The experiments show an encouraging result as it outperforms the CSP-based extractor algorithm (95% and 96% of recall and precision, respectively). Moreover, it gives a high performance result when tested on the SWDE benchmark dataset (84.91%).

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually revised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the performance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.

Abstract Named Entity Recognition (NER) is an essential task for many natural language processing systems, which makes use of various linguistic resources. NER becomes more complicated when the language in use is morphologically rich and structurally complex, such as Arabic. This language has a set of characteristics that makes it particularly challenging to handle. In a previous work, we have proposed an Arabic NER system that follows the hybrid approach, i.e. integrates both rule-based and machine learning-based NER approaches. Our hybrid NER system is the state-of-the-art in Arabic NER according to its performance on standard evaluation datasets. In this article, we discuss a novel methodology for overcoming the coverage drawback of rule-based NER systems in order to improve their performance and allow for automated rule update. The presented mechanism utilizes the recognition decisions made by the hybrid NER system in order to identify the weaknesses of the rule-based component and derive new linguistic rules aiming at enhancing the rule base, which will help in achieving more reliable and accurate results. We used ACE 2004 Newswire standard dataset as a resource for extracting and analyzing new linguistic rules for person, location and organization names recognition. We formulate each new rule based on two distinctive feature groups, i.e. Gazetteers of each type of named entities and Part-of-Speech tags, in particular noun and proper noun. Fourteen new patterns are derived, formulated as grammar rules, and evaluated in terms of coverage. The conducted experiments exploit a POS tagged version of the ACE 2004 NW dataset. The empirical results show that the performance of the enhanced rule-based system, i.e. NERA 2.0, improves the coverage of the previously misclassified person, location and organization named entities types by 69.93 per cent, 57.09 per cent and 54.28 per cent, respectively.

Abstract A spelling error detection and correction application is typically based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system. We develop our dictionary of 9.2 million fully-inflected Arabic words (types) from a morphological transducer and a large corpus, validated and manually revised. We improve the error model by analyzing error types and creating an edit distance re-ranker. We also improve the language model by analyzing the level of noise in different data sources and selecting an optimal subset to train the system on. Testing and evaluation experiments show that our system significantly outperforms Microsoft Word 2013, OpenOffice Ayaspell 3.4 and Google Docs.

Question Answering Systems (QASs) have emerged as a good alternative for information seekers to retrieve precise information over the Internet. A good amount of research has been done to improve the performance of QASs across several languages, including European and Asian languages. However, Arabic, a morphologically rich Semitic language spoken by over 422 million people, has not seen similar development in the field of question answering. This article reviews the developments taking place in Arabic QASs as well as the challenges faced by researchers in developing Arabic QASs. After conducting an extensive literature survey of a number of English and Arabic QASs, this article classifies them according to several criteria. The most commonly used architecture for the development of an Arabic QAS, known as pipeline architecture, has been presented. In order to encourage and support the new researchers and scholars in conducting research in Arabic QASs, a list of techniques, tools, and computational linguistic resources, required to implement the components of the presented pipelined architecture, are described in this article in a simple and persuasive manner. Finally, the gap analysis between the research in Arabic and English QASs has been performed and accordingly, some future directions for research in Arabic QASs have been proposed.

The social media is gaining a lot of importance among businesshouses, academicians, medical practitioners, politicians, among others, due to its role in creating awareness about products, services, and socio-political views. The end users of these products, services, and views provide their feedbacks in the form of comments. An accurate determination of the sentiments of end users is crucial in designing policies and plans for products and services in future. As the processing power and storage capacities of computers have increased several folds, researchers can focus more on the accuracy of sentiment detection than consumption of computational resources. In this paper, we are applying a set of heuristics to analyse sentiments using freely available dictionary resources and open source tools. We have tested these heuristics over a large data set collected from standard sources. The experimental results are promising and opening new research directions in dictionary-based sentiment analysis.

Abstract Arabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners’ errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate.

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.

The advances in information and communication technologies have changed the way the governments interact with their citizens. The development of the internet and its vast capabilities played a vital role in this change. According the UN report 2010, the UN e-Government development index of the world rated United Arab Emirates in 49 which comes late 17 places than 2008 ranking. During 2008 the UAE ranked in 32. Therefore, it can be seen that UAE is having some troubles in citizens' adoption of the e-Government. In this paper, we will examine the key berries to the use of the e-Government services by citizens through testing the effect of 11 independent variables on the citizens' use of e-Government in municipal of Emirate of Abu Dhabi.

In the context of e-health, professionals and healthcare service providers in various organisational and geographical locations are to work together, using information and communication systems, for the purpose of providing better patient-centred and technology-supported healthcare services at anytime and from anywhere. However, various organisations and geographies have varying contexts of work, which are dependent on their local work culture, available expertise, available technologies, people's perspectives and attitudes and organisational and regional agendas. As a result, there is the need to ensure that a suggestion - information and knowledge - provided by a professional to support decision making in a different, and often distant, organisation and geography takes into cognizance the context of the local work setting in which the suggestion is to be used. To meet this challenge, we propose a framework for context-aware knowledge modelling in e-health, which we refer to as ContextMorph. ContextMorph combines the commonKADS knowledge modelling methodology with the concept of activity landscape and context-aware modelling techniques in order to morph, i.e. enrich and optimise, a knowledge resource to support decision making across various contexts of work. The goal is to integrate explicit information and tacit expert experiences across various work domains into a knowledge resource adequate for supporting the operational context of the work setting in which it is to be used.

Abstract Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule‐based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill‐formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, nonstandardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F‐measure.

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.

introduction Share on Introduction to the Special Issue on Arabic Natural Language Processing Authors: K. Shaalan The British University in Dubai The British University in DubaiView Profile , A. Farghaly Monterey Institute of International Studies Monterey Institute of International StudiesView Profile Authors Info & Claims ACM Transactions on Asian Language Information ProcessingVolume 8Issue 4December 2009 Article No.: 13pp 1–3https://doi.org/10.1145/1644879.1644880Published:01 December 2009Publication History 1citation365DownloadsMetricsTotal Citations1Total Downloads365Last 12 Months9Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

Arabic is a Semitic language that is rich in its morphology and syntax. The very numerous and complex grammar rules of the language may be confusing for the average user of a word processor. In this paper, we report our attempt at developing a grammar checker program for Modern Standard Arabic, called Arabic GramCheck. Arabic GramCheck can help the average user by checking his/her writing for certain common grammatical errors; it describes the problem for him/her and offers suggestions for improvement. The use of the Arabic grammatical checker can increase productivity and improve the quality of the text for anyone who writes Arabic. Arabic GramCheck has been successfully implemented using SICStus Prolog on an IBM PC. The current implementation covers a well-formed subset of Arabic and focuses on people trying to write in a formal style. Successful tests have been performed using a set of Arabic sentences. It is concluded that the approach is promising by observing the results as compared to the output of a commercially available Arabic grammar checker. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract This paper presents a lexical analyser for inflected Arabic words. An augmented transition network (ATN) technique was used to represent the context‐sensitive knowledge about the relation between a stem and inflectional additions. An exhaustive‐search algorithm is developed to traverse the ATN, generating all possible interpretations of an inflected Arabic word. The arcs of the ATN are augmented with rules containing conditions and actions. More than one rule is associated with some arcs. The states of the ATN are represented by Pascal procedures.