Arabic Sentences Research Articles

Syntactic Analysis of Arabic Adverb′s between Arabic and English: X Bar Theory

This study was undertaken to highlight some linguistic devices of structural and syntactic analysis of multi-position Arabic adverbs, the study offered a syntactic baseline between Arabic and English, the researcher selected six sentences of Arabic and English adverbs, 3 in Arabic and 3 in English, in order to serve the task of analysis and scrutinize some indiscernible adverbs loci of Arabic comparing with one key position in English. The main purpose of this study was to investigate whether the locus of adverbial sentence in Arabic is matchable to locus of English sentence or otherwise. The subject of this study was the flexible position of Arabic adverbs, which may approach in the initial, middle or even end of Arabic sentence. Towards that end, a comparative methodology of the Arabic and English adverbs have explicated some declarative and interrogative sentences from Arabic and its correspondence in English, via adopted chomeskyan syntactic theory (Chomsky, 1995, Minimalist Program) with paying heed to the syntactic similarities and dissimilarities of adverbs locus in both systems and delve for the flexible position in Arabic which could change the sentential meaning and the grammatical structure by adopting Chomsky X’ theory (1995).

Arabic to English Machine Translation of Verb Phrases Using Rule-Based Approach

Problem statement: Scientific translation represents an important stre am in the current century due to explosion of the information revolut ion. The translation of scientific text is still li mited in accuracy due to the fact that the scientific ter ms cannot be translated appropriately. Word order r ules are very important for the generation of sentences in the target language whereas the word order in Arabic language is different from the order in Engl ish. Any Arabic Machine Translation (MT) system to English should be able to deal with word order. Approach: The aim of this study is to introduce- MT (Verbal Sentence rule based Machine Translation), an automatic system for Arabic verbal sentence of scientific text to English translation using tra nsfer based approach. Verbal sentences constitute t he majority of Arabic scientific documents. The system involves three phases: analysis, transfer and a generation phase. The transfer method is one of the rule based approach category and the most common technique used in machine translation system. Results: The system was trained on 45 verbal sentences from different Arabic scientific t ext and tested on 30 new verbal sentences from different domains. An experiment performed involves comparison with two other machine translation systems namely Syzran and Google. The accuracy of the result of the designed system is 93%. Conclusion: VS-MT has been successfully implemented and tested on many verbal sentences from different field of Arabic thesis. An experimen t was performed which involves comparison with two other machine translation systems namely Syzran and Google. Our approach is efficient enough to translate Arabic verbal sentences of scientific tex t to English.

Context-Free Grammar Analysis for Arabic Sentences

This paper presents a simple parser to parse Arabic sentences. The aim of this parser is to check whether the syntax of an Arabic sentence is grammatically correct or not by constructing new efficient Context-Free Grammar that makes Top-Down technique much valuable. A set of experiments were ran on a dataset contains 150 Arabic sentence. The system achieved an average accuracy of 95%.

Arabic sign language continuous sentences recognition using PCNN and graph matching

Many previous researchers have tried developing sign languages recognition systems in general and Arabic sign language specifically. They succeeded to achieve acceptable results for isolated gestures level, but none of them investigated the recognition of connected sequence of gestures. This paper focuses on how to recognize real-time connected sequence of gestures using graph-matching technique, also how the continuous input gestures are segmented and classified. Graphs are a general and powerful data structure useful for the representation of various objects and concepts. This work is a component of a real-time Arabic Sign Language Recognition system that applied pulse-coupled neural network for static posture recognition in its first phase. This work can be adapted and applied to different sign languages and other recognition problems.

Grammatical Relation Extraction in Arabic Language

Problem statement: Grammatical Relation (GR) can be defined as a linguistic relation established by grammar, where linguistic relation is an association among the linguistic forms or constituents. Fundamentally the GR determines grammatical behaviors such as: placement of a word in a clause, verb agreement and the passivity behavior. The GR of Arabic language is a necessary prerequisite for many natural language processing applications, such as machine translation and information retrieval. This study focuses on the GR related problems of Arabic language and addresses the issue with optimum solution. Approach: We had proposed a rule based production method to recognize Grammatical Relations (GRs), as the rule-based approach had been successfully used in developing many natural language processing systems. In order to eradicate the problems of sentence structure recognition, the proposed technique enhances the basic representations of Arabic language such as: Noun Phrase (NP), Verb Phrase (VP), Preposition Phrase (PP) and Adjective Phrase (AP). We had implemented and evaluated the Rule-Based approach that handles chunking and GRs of Arabic sentences. Results: The system was manually tested on 80 Arabic sentences, with the length of each sentence ranging from 3-20 words. The results had yielded the F-score of 83.60%. This outcome proves the viability of this approach for Arabic sentences of GRs extraction. Conclusion: The main achievement of this study is development of Arabic grammatical relation extractions based ob rule-based approaches.

AL-TARAKIIB AL-‘ARABIYYAH AL-MUSTA’MALAH FII AL-LUGHAH AL-INDUUNISIYYAH: ‘ADDADUHA WA ANWAA’UHA WA KHASHAAISHUHAA

This article describes Arabic phrase and sentence adopted from Bahasa Indonesia to answer three main questions on language absorption, mainly: how many Arabic phrase and sentences adopted in Bahasa Indonesia? What kind of Arabic phrases and sentences adopted in Bahasa Indonesia? What are the changes in the Arabic phrases and sentences adopted in Bahasa Indonesia? This is a library research by collecting all the Arabic phrases and sentences found in Bahasa Indonesia compiled in the main resources, “Kamus Bahasa Indonesia Kontemporer” by Drs. Peter Salim and Yenny Salim, books on religion, literature, magazine, newspaper, speech in Bahasa Indonesia and conversation among speaker of Bahasa Indonesia as the secondary resource. The finding of research says that the number of Arabic phrase and sentences adopted in Bahasa Indonesia is 505 phrases or idiomatic sentences. The phrases and sentences have been adjusted in three aspects, mainly: phonetic, syntactical and semantic.<br />Keywords: Tarakiib, Ta’tsiir, Al-Lughah Al-Indunisiyyah<br /><br />

SENTENCE IN THE ARABIC LANGUAGE- SOME REMARKS ON CRITERIA OF MODERN DIVISIONS AND CLASSIFICATIONS

Modern syntactical researches conducted by Arabs are under great influence of traditional grammar. That as a consequence has various methodological approaches to interpretation of a sentence, its definitions and classifications. Even though some classical Arabic grammarians make semantic distinction between terms ğumla and kelam, today they are perceived as synonyms, under the influence of contemporary linguistic developments. Modern Arabic syntax does not strictly define criteria of classification of an Arabic sentence. In addition to traditional classification to a nominal and verbal sentence, the classification supported and accepted as the only or the most significant one by most Arabic authors, these criteria vary from one author to another. However, it is noticeable that the criterion of functionality, criterion of predicativity, structure and declinability of a sentence are distinguished as contemporary norms in syntactic classifications of the Arabic language. Oriental writings in classification of Arabic sentences often use conventions of foreign languages and apply criteria that are not characteristic of the Arabic language. That does not reflect the actual state of Arabic syntax and it also belittles efforts and contributions of Arabic grammarians in this field. Keywords: Arabic syntax, sentence, divisions and types of sentences, Orientalistic attitudes, comparative analysis

Rule Based Shallow Parser for Arabic Language

Problem statement: One of language processing approaches that compute a basic analysis of sentence structure rather than attempting full syntactic analysis is shallow syntactic parsing. It is an analysis of a sentence which identifies the constituents (noun groups, verb groups, prepositional groups), but does not specify their internal structure, nor their role in the main sentence. The only technique used for Arabic shallow parser is Support Vector Machine (SVM) based approach. The problem faced by shallow parser developers is the boundary identification which is applied to ensure the generation of high accuracy system performance. Approach: The specific objective of the research was to identify the entire Noun Phrases (NPs), Verb Phrases (VPs) and Prepositional Phrases (PPs) boundaries in the Arabic language. This study discussed various idiosyncrasies of Arabic sentences to derive more accurate rules to detect start and the end boundaries of each clause in an Arabic sentence. New rules were proposed to the shallow parser features up to the generation of two levels from full parse-tree. We described an implementation and evaluate the rule-based shallow parser that handles chunking of Arabic sentences. This research was based on a critical analysis of the Arabic sentences architecture. It discussed various idiosyncrasies of Arabic sentences to derive more accurate rules to detect the start and the end boundaries of each clause in an Arabic sentence. Results: The system was tested manually on 70 Arabic sentences which composed of 1776 words, with the length of the sentences between 4-50 words. The result obtained was significantly better than state of the art Arabic published results, which achieved F-scores of 97%. Conclusion: The main achievement includes the development of Arabic shallow parser based on rule-based approaches. Chunking which constitutes the main contribution is achieved on two successive stages that include grouped sequences of adjacent words on the basis of linguistic properties.

Pitfalls Encountered by Bilingual Arab Learners in Translating the Arabic Discourse Marker 'θumma' into English

Modern Standard Arabic (henceforth, MSA) is characterized as syndetic and formulaic language. The current study aimed at investigating the problems that Arab learners of English and translators encounter in translating sentences holding the discourse marker (henceforth, DM) θumma into English. This may be especially true if these languages are genetically unrelated, as is the case with Arabic and English. Five functions [sequence with span of time, sequence with no span of time, resumption, adversative and consequence] were identified through surveying various kinds of MSA texts. A translation task of 40 Arabic sentences holding the discourse marker θumma was performed by a random sample of 55 senior students with similar linguistic, sociocultural and educational backgrounds studying at Al-Balqa' Applied University-Jordan. Findings revealed that the improper translation of functions of the Arabic DM θumma is still prominent among Arab learners of English even at advanced stages of their learning. The pitfalls were ranked hierarchically in terms of difficulty. Findings of the study were discussed, conclusions had been drawn, implications and future directions were provided at the end.

An Adaptive Parser for Arabic Language Processing

In this study, we present a robust bottom-up Arabic parser that investigates the correctness of Arabic sentences by passing them through a set of predetermined states relying on their individual words. The major benefit of our approach is the reduced number of backup states tested when determining the grammatical structure of a given sentence. The proposed approach is optimized to tokenize the input sentences correctly since accurate tokenization is the essential step of the parser; this process also reduces the parsing time. Our proposed parser is extendable; hence, it allows new words to be added to the lexicon, i.e. the lexicon is built dynamically. Experimental results have demonstrated the effectiveness of our approach in checking correctly numerable sentences with different lengths. The accuracy was 85.88% when tested on a sample of 170 Arabic sentences taken from an existing Arabic text taught in k-12 grade levels.

English to Arabic Machine Translation Based on Reordring Algorithm

Problem statement: The purpose of Machine Translation (MT) is set accurate translation by implement a system to achieve similar result with human translates as in case the translating English to Arabic. The translation is a creative process which involves interpretation of the text that given by the translator. In addition, the translation depends on the purpose for which it meant according to context, as well as the text context includes different size and types of sentences. In addition, English words order in target language is not the same order as the Arabic source language, so the translation from English to Arabic definitely needs effort to synchronize words between English and Arabic that based on matching rules grammar between both languages. Approach: This study focuses on the existing Context Free Grammar (CFG) format as well as identifying the Part Of Speech (POS) for single words and reordering the CFG for reorder purpose of English structure to Arabic structure and validate the reorder CFG constructed by the algorithm. Reordering algorithm is a system for translating structured English sentence in text to structure Arabic sentence in text that runs compatibly with English/Arabic interface. English dictionary is used to translate single word consist of only word categories (POS) and Bi-lingual dictionary is used to translate single word consist of only word meaning relative to categories in approach format. Results: The domain area include twenty (20) abstracts containing ninety five (95) sentences have been tested in order to verify the authenticity of computer translation algorithm and the result were compared with human translation. The results obtain shows that the reordering rules is 81.855% accuracy on a translation from English Language to Arabic using an abstract from the European Psychiatry Journal. Conclusion: Based on the achieved results, we have managed to perform the syntactic reordering within an English sentence in text (abstract) to Arabic translation task by using Reordering Algorithm.

Study on the Epistemic modality in Arabic modal sentence

Study on the Epistemic modality in Arabic modal sentence

An Ellipsis Resolution System for the Arabic Language

The ellipsis phenomenon constitutes one of the important topics of study in natural language processing because it appears frequently in dialogues as well as in written texts. This is the context of the present article which proposes an ellipsis processing approach for the Arabic language. Our first contribution consists of introducing a formal characterisation of the ellipsis phenomenon, which constitutes the basis of the method proposed for detection of elliptical sentence parts. Then we present a clause grammar that makes it possible to distinguish between well-formed clauses and those with missing constituents. Concerning the resolution, the proposed method relies on an elliptical sentence classification underlying these three different resolution processes: using propagation, cascaded, and alternation. In this paper, we also try to resolve some ambiguities concerning ellipsis resolution and to study the phenomenon of anaphora, which can interact with ellipsis. To prove the feasibility of the proposed approaches, we have developed a prototype called ERASE (Ellipsis Resolution of Arabic Sentences) and we have tested it on a corpus of elliptical Arabic sentences. The results obtained are satisfactory.

Statistical Part-of-Speech Tagger for Traditional Arabic Texts

Problem statement: This study presented the development of an Arabic part-of-speech tagger that can be used for analyzing and annotating traditional Arabic texts, especially the Quran text. Approach: It is a part of a project related to the computerization of the Holy Quran. One of the main objectives in this project was to build a textual corpus of the Holy Quran. Results: Since an appropriate textual version of the Holy Quran was prepared and morphologically analyzed in other stages of this project, we focused in this work on its annotation by developing and using an appropriate tagger. The developed tagger employed an approach that combines morphological analysis with Hidden Markov Models (HMMs) based-on the Arabic sentence structure. The morphological analysis is used to reduce the size of the tags lexicon by segmenting Arabic words in their prefixes, stems and suffixes; this is due to the fact that Arabic is a derivational language. On another hand, HMM is used to represent the Arabic sentence structure in order to take into account the linguistic combinations. For these purposes, an appropriate tagging system has been proposed to represent the main Arabic part of speech in a hierarchical manner allowing an easy expansion whenever it is needed. Each tag in this system is used to represent a possible state of the HMM and the transitions between tags (states) are governed by the syntax of the sentence. A corpus of some traditional texts, extracted from Books of third century (Hijri), is manually morphologically analyzed and tagged using our developed tagset. Conclusion/Recommendations: It is then used for training and testing this model. Experiments conducted on this dataset gave a recognition rate of about 96% and thus are very promising compared to the data size tagged till now and used in the training. Since our Holy Quran corpus is still under revision, we did not make significant experiments on it. However, preliminary tests conducted on the seven verses of AL-Fatiha showed an encouraging accuracy rate.

الخبر في البلاغة العربية

The science of ma’ânî is a set of rules and of principles employed to know how to make an Arabic sentence structure agreeing with language situation and condition. One of the objects of the science of ma’ânî is khabar. Khabar is a sentence that may be either true or false, depending on the reality. If the reality agrees with khabar, then it is called true khabar, but if not, it is called false khabar. There are two purposes of delivering khabar, namely fâ’idat al-khabar and lâzim al-fâ’idah. Sometimes the purposes of khabar deviate from that two original purposes to become istirhâm, izhâr al-da’f, izhâr al-tahassur, etc.Kata Kunci: al-balâghah al-‘arabiyyah, ‘ilm al-ma’ânî, al-khabar

An HPSG approach to Arabic nominal sentences

AbstractThis paper introduces a grammar for Arabic nominal sentences written in the formalism of Head‐Driven Phrase Structure Grammar (HPSG). This grammar covers simple Arabic nominal sentences, though the same approach can be extended to cover other types of Arabic nominal sentences. The formalization has been implemented using the Linguistic Knowledge Building (LKB) system.

The effect of font size and type on reading performance with Arabic words in normally sighted and simulated cataract subjects

Background: Previous investigations have shown that reading is the most common functional problem reported by patients at a low vision practice. While there have been studies investigating effect of fonts in normal and low vision patients in English, no study has been carried out in Arabic. Additionally, there has been no investigation into the use of optimum print sizes or fonts that should be used in Arabic books and leaflets for low vision patients.Methods: Arabic sentences were read by 100 normally sighted volunteers with and without simulated cataract. Subjects read two font types (Times New Roman and Courier) in three different sizes (N8, N10 and N12). The subjects were asked to read the sentences aloud. The reading speed was calculated as number of words read divided by the time taken, while reading rate was calculated as the number of words read correctly divided by the time taken.Results: There was an improvement in reading performance of normally sighted and simulated visually impaired subjects when the print size increased. There was no significant difference in reading performance between the two types of font used at small print size, however the reading rate improved as print size increased with Times New Roman.Conclusion: The results suggest that the use of N12 print in Times New Roman enhanced reading performance in normally sighted and simulated cataract subjects.

Semantic Representation of Arabic: A Logical Approach towards Compositionality and Generalized Arabic Quantifiers

Research on semantic processing of Arabic language and in particular on deep semantic analysis considering the compositionality of its syntactical structure has so far received very little computational research. This work is therefore attempting to fill some essential aspects of this gap in introducing a logic-based compositional model covering fundamental issues involved in semantic analysis of Arabic sentences. The focus of attention is relying on studying the compositionality of important Arabic syntactical constituents and on extending the concept of the generalized natural language quantification to Generalized Arabic Quantifiers GAQ utilizing lambda-calculus and the type theoretical analysis of Arabic structure. Since semantic representation has to be compositional in natural language understanding systems this approach attempts to propose an element framework for developing more practical and intelligent Arabic natural language processing systems.

Sentence alignment using P-NNT and GMM

Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align English–Arabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel English–Arabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moore’s approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese–Chinese texts, than the ones used in the current research.

Difficulties Encountered by Bilingual Arab Learners in Translating Arabic ‘fa’ into English

This study investigates the problems that translators and Arab learners of English encounter in translating Arabic sentences containing the Arabic discourse marker ‘fa’ into English. Several types of texts were surveyed in order to identify the salient functions that this marker has in Arabic discourse. Five major functions were identified: Explanatory, Consequential, Causal, Sequential and Adversative. A translation task was designed to find out how this marker translates into English. The subjects were 50 English-major Arab students studying at the University of Sharjah. The difficulties that Arab learners encounter in translating Arabic ‘fa’ into English were identified and rank ordered in terms of difficulty.