Grammatical construction of function words between old and modern written Arabic: A corpus-based analysis

In this paper, we investigate the form, salient patterns and core functions of word-level total reduplication in Classical Arabic (CA) and Modern Standard Arabic (MSA). Using a multi-genre corpus of 469 million words, we extract total reduplication (TR) candidates into an SQL database, manually filter them, and perform concordance search to identify the patterns and functions. Data analysis reveals nine patterns and eleven functions of TR and compares their relative frequency in each variety. The functions of TR are mapped into two broad categories: morphological and semantic/pragmatic. Results show an interesting variation in terms of top functions being favored by the two varieties. While TR is favored by CA to express serial ordering, MSA is noticed to favor it to express intensification. The empirical findings of this study provide a reliable quantification of the status of TR in CA and MSA which is rather difficult to obtain by theoretical means: on the one hand, TR in Arabic is not as productive as in other languages such as Indonesian. On the other hand, it is more common in CA than in MSA because the latter usually resorts to using loose phrases to express the same concepts expressed by TR in CA.

This study investigates eye movement patterns as indicators of text complexity in Arabic, focusing on the comparative analysis of Classical Arabic (CA) and Modern Standard Arabic (MSA) text. Using the AraEyebility corpus, which contains eye-tracking data from readers of both CA and MSA text, we examined differences in fixation patterns, regression rates, and overall reading behavior between these two forms of Arabic. Our analyses revealed significant differences in eye movement metrics between CA and MSA text, with CA text consistently eliciting more fixations, longer fixation durations, and more frequent revisits. Multivariate analysis confirmed that language type has a significant combined effect on eye movement patterns. Additionally, we identified different relationships between text features and eye movements for CA versus MSA text, with sentence-level features emerging as significant predictors across both language types. Notably, we observed an interaction between language type and readability level, with readers showing less sensitivity to readability variations in CA text compared to MSA text. These findings contribute to our understanding of how historical language evolution affects reading behavior and have practical implications for Arabic language education, publishing, and assessment. The study demonstrates the value of eye movement analysis for understanding text complexity in Arabic and highlights the importance of considering language-specific features when studying reading processes.

Arabic natural language processing: An overview

Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for the translation of texts of social media. More precisely, this paper focuses on the Tunisian Dialect of Arabic (TAD) with an application on automatic machine translation for a social media text into MSA and any other target language. Linguistic tools such as a bilingual TAD-MSA lexicon and a set of grammatical mapping rules are collaboratively constructed and exploited in addition to a language model to produce MSA sentences of Tunisian dialectal sentences. This work is a first-step towards collaboratively constructed semantic and lexical resources for Arabic Social Media within the ASMAT (Arabic Social Media Analysis Tools) project.

Subject Verb Agreement in Modern Standard Arabic (MSA) and Saudi Dialect of Arabic (SA): A New Minimalist Account Khalaf M. J. AlShammiry Abstract Using Chomsky’s theory of syntax, the Minimalist Program (MP) (1993, 1995, 2000), this paper presents a new analysis for the ongoing debatable word order variation in both Modern Standard Arabic (MSA) and the Saudi dialect of Arabic (SA). It has been noticed that in SVO order, the subject agrees partially with the verb; whereas in VSO order, the subject fully agrees with the verb. In SA, the subject always fully agrees with the verb in both word orders. Previous studies have debated whether the preverbal subject is a subject or a topic or a focus. I argue that the noun in the MSA and SA is a subject which is either base generated in the case of the MSA or moved from inside the clause in the case of SA; making use of the distinction between al-jumlaalismiyya “ the nominal sentence” and al-jumlaal-fi?liyya “the verbal sentence” in Arabic, Abd Al-bidee (1996) and among others, I ague that there is a Head Phrase (HP) at the left periphery of the clause where the subject surfaces to check the nominal head feature of the HP and to mark the clause nominal. For this argument, I assume that the clitic onto the verb is either an agreement marker or a subject and the number feature is weak in MSA and strong in SA. In addition, before the syntax, the noun is split into an expletive and a noun or a clitic and the TP in SA has two specs. The two supporting pieces of evidence for my argument are the subject of the embedded clauses and the structure of the wh-questions in MSA. The contribution of the paper lies in the fact that it investigates the agreement issue in a dialect that is, to my knowledge, heretofore uninvestigated; and adds to our understanding of the syntax of the agreement asymmetry in MSA and SA. Full Text: PDF DOI: 10.15640/jflcc.v4n2a8

In Natural Language Processing (NLP) studies, developing resources and tools makes a contribution to extension and Effectiveness of researches in each language. In recent years, Arabic Named Entity Recognition (ANER) has been considered by NLP researchers. While most of these researches are based on Modern Standard Arabic (MSA), in this paper, we focus on Classical Arabic (CA) literature. We propose a corpus called NoorCorp with 200k labeled words for research purposes which is annotated by expert human resources manually. We also collected about 18k proper names from old Hadith books as gazetteer which is called NoorGazet. Using ensemble learning, we develop a new approach for extraction of named entities (NEs) including person, location and organization. Adaboost.M2 algorithm, as implementation of multiclass Boosting method, is applied to train the prediction model. Results show that performance of the method is better than decision tree as the base classifier. We have used tokenizing, part of speech (POS) tagging, and base phrase chunking (BPC) to overcome linguistic obstacles in Arabic. An overall F-measure value of 96.04 is obtained. In addition, we have studied the effect of preprocessing and external resources on the system results. Finally, the proposed approach is applied on ANERCorp as MSA corpus and we have compared the results with NoorCorp.

Kuwaiti Arabic (KA), like other Arabic dialects, is a spoken variety of Arabic that does not have a standardized written convention contrary to Modern Standard Arabic (MSA). With the emergence and spread of social media platforms, Arabic dialects have found their way into the written medium, and hence a need arose to process them alongside MSA. The biggest challenge facing NLP tools is that dialects do not have consistent written conventions contrary to MSA, and writers expressing their dialects usually follow a phonetic writing system, or they write words as they pronounce them. This has opened the door for variations within the same dialect and between dialects and MSA. Furthermore, a prerequisite for analysing any language or dialect is the presence of clear written conventions. Therefore, efforts have been made to establish written conventions for Arabic dialects, but the Kuwaiti dialect has not received the required attention. The current study offers a practical solution for processing written KA. It identified and extracted the written conventions of KA from natural data collected from over 100K Kuwaiti tweets since they represent a good model of natural language. The morphological analyzer (MADAMIRA) - which is designed to process MSA - was enhanced with the extracted criteria. Furthermore, the study involved enriching the analyzer with a dictionary of Kuwaiti terms and vocabulary ‘lemmas’ collected from the Encyclopaedia of Kuwaiti Arabic and from the most used Kuwaiti words on Twitter (currently X). Providing the analyzer with this dictionary of KA words helps it process KA more accurately. The expanded version of the analyzer (MADAMIRA-KA) is the first of its kind designed entirely to process the Kuwaiti dialect and has achieved excellent performance in analyzing over 100K Kuwaiti tweets successfully. The importance of this study lies in developing such a morphological analyzer, which can be used for automated translation, dialect recognition and sentiment analysis.

Named entities recognition (NER) has become, over time, a potentially helpful pre-processing for several NLP tasks. It mainly identifies and classifies entities in the text into predefined categories, such as a person, location, and organization. Most of the work done in Arabic named entity recognition (ANER) has focused on Modern Standard Arabic (MSA). However, most scripts on the internet, especially in social media (which are currently a source for corpus development), are in a dialectal form that does not follow standard writing rules. This paper investigates the possibility of deep learning based on named entity recognition in Algerian dialect script through a comparative study between 5 models: AraBERT, DziriBERT, MARBERT, ARBERT, and mBERT. We chose these five models for two significant reasons; the first one, that these models are already pre-trained on the MSA and Arabic dialect text, the second reason, that they have proved their efficiency for other tasks such as Part of Speech Tagging, sentiment analysis, and ANER.KeywordsNLPDeep learningAlgerian dialectNamed Entity RecognitionSocial media

The study investigated the acquisition of Modern Standard Arabic (MSA) by second language (L2) learners and by heritage speakers of the colloquial varieties of Arabic. The study focused on three questions: (1) whether heritage speakers who enroll in college‐level elementary MSA classes have an advantage over their L2 counterparts, (2) whether any potential advantage that heritage speakers may bring into the classroom carries on to a later stage of their MSA learning, and (3) the role of positive or negative transfer from the colloquial variety in the acquisition of MSA in a formal setting. Thirty‐five heritage speakers (19 elementary and 16 advanced), 28 advanced L2 learners, and 16 native‐speaker controls completed five negation tasks. The results indicated that, whereas elementary heritage speakers entered the MSA classroom with an advantage over their L2 counterparts in the area under study, the advanced heritage speakers exhibited patterns that were comparable to those of the L2 learners. Unlike their L2 counterparts, however, the advanced heritage speakers displayed positive and negative transfer effects from colloquial Arabic. The findings are discussed in the framework of three models/hypotheses of first language transfer to a third language, and their pedagogical implications for Arabic language teaching and learning are explicated.

Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited. The scarcity of resources has prompted the use of Modern Standard Arabic (MSA) abundant resources to complement the limited dialectal resource. However, dialectal clitics often differ between MSA and DA. This paper compares morphology-aware DA word segmentation to other word segmentation approaches like Byte Pair Encoding (BPE) and Sub-word Regularization (SR). A set of experiments conducted on Egyptian Arabic (EA), Levantine Arabic (LA), and Gulf Arabic (GA) show that a sufficiently accurate morphology-aware segmentation used in conjunction with BPE outperforms the other word segmentation approaches.

This article discusses about the ways how English teacher chooses reading texts for his/ her students. In selecting the texts, teacher pays attention to the words of the texts. The number of function and lexical words of the texts is considered in selecting texts. Function words are such as the, a, an, this, those, that and these. Lexical words are words that refer to someone, things, actions and properties. Key words: texts, function words, lexical words

ecently, the widespread use of social media has increased, leading to increased concerns about cyberbullying. It has become imperative to intensify efforts and methods to detect and manage cyberbullying through social media. Arabic has recently received increasing attention to improve the classification of Arabic texts. Given the multitude of Arabic dialects used on social media platforms by Arabic speakers to express their opinions and communicate with each other, applying this approach to Arabic becomes extremely challenging due to its structural and morphological complexity. Analyzing Arabic dialects using Natural Language Processing (NLP) tools can be more challenging than Standard Arabic. In this paper, the impact of using stopword removal and derivation techniques on detecting cyberbullying in the Libyan dialect was presented. The efficiency of text classification was compared when using a Libyan dialect word list alongside pre-generated Modern Standard Arabic (MSA) lists. The texts were classified using Convolutional Neural Network (CNN) classifiers, and the experiments showed that when using Libyan dialect words, the accuracy results were 92% and 83%, and when using only Standard Arabic stop words, the accuracy results were dropped to 91% and 77%. Based on these results, the higher accuracy was obtained when using the presented stop words list which it is specific to the Libyan dialect, and they had a positive impact on the results, better than Standard Arabic stop words.

We present in this chapter a practical approach in building Arabic automatic speech recognition (ASR) system for mobile telecommunication service applications. We also present a procedure in conducting acoustic modelling adaptation to better take into account the pronunciation variation across the Arabic speaking countries. Modern Standard Arabic (MSA) is the common spoken and written language for all the Arab countries, ranging from Morocco in the west to Syria in the East, including Egypt, and Tunisia. However, the pronunciation varies significantly from one country to another to a degree that two persons from different countries may not be able understand each other. This is because Arabic speaking countries are characterized by a large number of dialects that differ to an extent that they are no longer mutually intelligible and could almost be described as different languages. Arabic dialects are often spoken rather than written varieties. MSA is common across the Arab countries, but it is often influenced by the dialect of the speaker. This particularity of the Arabic countries constitutes a practical problem in the development of a speech-based application in this region; suppose a speech application system is built for one country influenced by one dialect, what does it take to adapt the system to serve another country with a different dialect region? This is particularly challenging since resource to build accurate speaker independent Arabic ASR system for mobile telecommunication service applications are limited for most of the Arabic dialects and countries. Recent advances in speaker independent automatic speech recognition (SI-ASR) have demonstrated that highly accurate recognition can be achieved, if enough training data is available. However, the amount of available speech data that take into account the dialectal variation of each Arabic country is limited, making it challenging to build a high performance SI-ASR system, especially when we target specific applications. Another big challenge when building an SI-ASR is to handle speaker variations in spoken language. These variations can be due to age, gender, educational level as well as the dialectical variants of Arabic language. Usually an ASR system trained in one regional variation exhibits poorer performance when applied to another regional variation. Three problems may arise when a SI-ASR system built for one dialect but applied to target users with a different dialect: (1) Acoustic model mismatch, (2) Pronunciation lexicon mismatch and (3) Language model mismatch.

The present chapter deals with the coarticulatory effects of the contrast of pharyngealisation in Modern Standard Arabic (MSA) and Dialectal Arabic (DA). The first part of the study reports acoustic data, based on locus equation (LE) parameters extracted from the production of sixteen subjects from Yemen, Kuwait, Jordan and Morocco who spoke 24 words in MSA and 24 words in DA, with a symmetrical VCV context [iCi, uCu, aCa] where C was either pharyngealised /tˁ, dˁ, sˁ, ðˁ/ or non-pharyngealised /t, d, s, ð/. LE parameters were found to accurately reflect pharyngealisation patterns of CV coarticulatory effects according to speech variety and geographical clustering. The second part of the study deals with EMA data from one Tunisian speaker. It provides some observations of the articulatory mechanism of pharyngealisation. This section provides also a comprehensive interpretation of the coarticulatory effects of pharyngealisation as revealed by locus equation parameters.

Globalization has had an impact on the education system in the UAE, where increased use of bilingual curriculum (Arabic-English) is held in high regard. Nevertheless, literacy among Emirati children and teenagers remains low. This study uses a 15-item, open-ended questionnaire completed by Emirati parents and an 8-item, open-ended questionnaire completed by Emirati primary school children from 8 to 11 years of age, and compares the translation of The Little prince into Modern Standard Arabic (MSA) and into the Emirati dialect. The results suggest that not only the Emirati dialect, but any Arabic dialect (in its respective Arab country) could be used in primary schools to motivate children to read in Arabic and bridge the gap between their spoken language (dialect) and formal written Arabic. Not only must an Arab child learn how to read, but also, they have to understand a very formal language system that they are not used to speaking at home, i.e., Modern Standard Arabic (MSA). Having books in their own dialects may make children more interested in reading, as they can understand them more easily.