Modern Standard Arabic Text Research Articles

A Systemic Functional Analysis of Theme in Modern Standard Arabic Texts

The concept of Theme is regarded as a functional linguistic element that exists in many languages. The main aim of this study is to explore the functions of Theme in Arabic, applying the Systemic Functional Linguistics framework adopted by Downing (1991). Methodologically, several related real examples have been selected from the written discourse of Modern Standard Arabic and then analyzed contextually. The empirical analysis has revealed that (i) Theme can provide different functions, such as Individual, Circumstantial and Subjective and Logical Frameworks for the interpretation of the Rheme, and (ii) Theme can interact dynamically with different grammatical functions (e.g. Subject, Object, etc.) and have different pragmatic functions (e.g. Topic, Given and New information). Therefore, the view that makes a necessary link between Theme on the one hand and Noun Phrase, Topic or Given information on the other hand is proven incorrect and empirically invalid. Similar results have been obtained in the context of English (Downing 1991) but not yet for Arabic? This strengthens not only the universality of the concept of Theme but also its functions.

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Abstract Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence.

Arabic Word Segmentation With Long Short-Term Memory Neural Networks and Word Embedding

In this paper, we propose an Arabic word segmentation technique based on a bi-directional long short-term memory deep neural network. This paper addresses the two tasks of word segmentation only and word segmentation for nine cases of the rewrite. Word segmentation with a rewrite concerns inferring letters that are dropped or changed when the main word unit is attached to another unit, and it writes these letters back when the two units are separated as a result of segmentation. We only use binary labels as indicators of segmentation positions. Therefore, label 1 is an indicator of the start of a new word (split) in a sequence of symbols not including whitespace, and label 0 is an indicator for any other case (no-split). This is different from the mainstream feature representation for word segmentation in which multi-valued labeling is used to mark the sequence symbols: beginning, inside, and outside. We used the Arabic Treebank data and its clitics segmentation scheme in our experiments. The trained model without the help of any additional language resources, such as dictionaries, morphological analyzers, or rules, achieved a high F1 value for the Arabic word segmentation only (98.03%) and Arabic word segmentation with the rewrite (more than 99% for frequent rewrite cases). We also compared our model with four state-of-the-art Arabic word segmenters. It performed better than the other segmenters on a modern standard Arabic text, and it was the best among the segmenters that do not use any additional language resources in another test using classical Arabic text.

Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy

Text classification is the process of assigning a text or a document to various predefined classes or categories to reflect their contents. With the rapid growth of Arabic text on the Web, studies that address the problems of classification and segmentation of the Arabic language are limited compared to other languages, most of which implement word-based and feature extraction algorithms. This paper adopts a PPM character-based compression scheme to classify and segment Classical Arabic (CA) and Modern Standard Arabic (MSA) texts. An initial experiment using the PPM classification method on samples of text resulted in an accuracy of 95.5%, an average precision of 0.958, an average recall of 0.955 and an average F-measure of 0.954, using the concept of minimum cross-entropy. PPM-based classification experiments on standard Arabic corpora showed that they contained different types of text (CA or MSA), or a mixture of the both (CA and MSA). Further experiments with the same corpora showed that a more accurate picture of the contents of the corpora was possible using the PPM-based segmentation method. Tag-based compression experiments (using tags produced by parts-of-speech Arabic taggers) also showed that the quality of the tagging (as measured by compression quality) is significantly affected when tagging either CA and MSA text. The conclusion is that NLP applications (such as taggers) should treat these texts separately and use different training data for each or process them differently.

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.

A survey of automatic Arabic diacritization techniques

AbstractIn Modern Standard Arabic texts are typically written without diacritical markings. The diacritics are important to clarify the sense and meaning of words. Lack of these markings may lead to ambiguity even for the natives. Often the natives successfully disambiguate the meaning through the context; however, many Arabic applications, such as machine translation, text-to-speech, and information retrieval, are vulnerable due to lack of diacritics. The process of automatically restoring diacritical marks is called diacritization or diacritic restoration. In this paper we discuss the properties of the Arabic language and the issues that are related to the lack of the diacritical marking. It will be followed by a survey of the recent algorithms that were developed to solve the diacritization problem. We also look into the future trend for researchers working in this area.

The Dual Distal Demonstrative in Modern Standard Arabic: Between Norms and Non-Canonicity

The system of the demonstrative pronouns in Classical Arabic (CA), on which that of Modern Standard Arabic (MSA) is based, is distance-oriented and has been described as a two-term (proximal-distal) or three-term (proximal-medial-distal) system. A characteristic feature of the subsystem of the distal demonstratives in CA was that it had a suffix indicating the gender and number of the addressee(s). Within the subsystem of distal demonstratives, the dual form has not been described in a unanimous way: authors do not agree as to its form, some give more than one, others do not mention the issue, while according to some descriptions this form does not exist at all. In MSA texts, the function of the dual distal demonstrative is, however, sometimes carried out by a form which according to CA rules should be used when a singular object is pointed to and two persons are being addressed. This means that some users of MSA have reinterpreted the CA indicator of a dual addressee, -kumā, as the indicator of the duality of the object pointed to.

Towards including prosody in a text-to-speech system for modern standard Arabic

Most attempts to provide text-to-speech for modern standard Arabic (MSA) have concentrated on solving the problem of diacritic assignment (i.e. of recovering phonetically relevant information, such as choice of short vowels, which is not explicitly provided in the surface form of MSA). This is clearly a crucial issue: you can hardly produce intelligible spoken output if you do not know what the vowels are. We describe an approach to the task of generating speech from MSA text which not only solves this initial problem, but also provides the information required for imposing an appropriate intonation contour.