International Journal of Computer Processing of Languages

Searching for a pattern in an Arabic text raises various problems due to the association of vocalization characters with alphabetical letters of Arabic words. This feature causes a problem for existing searching algorithms. They either fail to find all partial matches of a pattern or they may suffer from performance degradation when they are simply modified to ignore these vocalization characters. This paper presents a new coding schema for Arabic vocalization characters that will facilitate and improve the performance of searching for vocalized and unvocalized patterns in any Arabic text (vocalized or unvocalized). This schema is based on repositioning the vocalization characters at the end of each word. We present in this paper the coding and decoding algorithms needed to support our new coding schema. In addition, we explain the modifications to the Boyer-Moore algorithm that take advantage of our improved coding schema together with the complexity analysis.

Compared to other major languages of the world, the Arabic language suffers from a dearth of research initiatives and research resources. As a result, Modern Standard Arabic (MSA) lacks reliable speech corpora for research in phonetics and related areas of linguistics. In recent years the Linguistic Data Consortium (LDC) published the first public MSA speech corpus designed for speech recognition experiments. That corpus was called West Point. Currently, we are using this corpus in our research experiments for speech recognition and other speech processing investigations. The aim of this paper is to evaluate the West Point Corpus from the MSA phonetic and linguistic point of view. The phonemes used and their numbers, the phoneme definitions, the labeling, and the scripts established by the West Point Corpus are included in the evaluation. Weaknesses, strengths, and discrepancies of the West Point Corpus regarding the linguistic rules and phonetic characteristics of MSA are also discussed in this paper.

In this paper, a novel approach to Arabic character recognition is proposed. The system is based upon the k-means algorithm and the k-nearest neighbors (k-NN) algorithm that employs the Mahalanobis distance measure. In the proposed system, each input character is mapped to its class using a minimum distance classifier. To demonstrate the merits of the proposed system, its performance is compared to that of a standard Support Vector Machine (SVM) solution. Simulation results have revealed that the proposed system has a lower time complexity and always produces lower error rates (higher success rates) than the current SVM solutions

The OCR output of scanned document images suffers from recognition errors especially when dealing with languages that are characterized by particularities and rich morphology such as the Arabic language, thus an effective error correction model is greatly needed. This paper focuses on three aspects of post-processing correction. First, improving the alignment and error n-gram models by adding correction rules based on character meta-classes rather than on specific characters, which is more suitable for the Arabic language. Second, using the language models to understand and correct the Arabic word fragment resulting from agglutinated affixes or isolated letters. The last will concern improving the language models by adding semantic information to the correction process, by using the bidirectional n-grams, stemming and removing stop words, which gives higher weights to n-grams sharing semantic meanings. In addition, we use a topic corpus, not a global one for a better probability distribution. The proposed model is effective in correcting the lexical errors and covered the semantic ones, that were not frequently reported by OCRs and are corrected after a manual proofreading. The proposed method shows an increase in the correction rate of almost 13% especially in meaningful terms.

Arabic Numerals are one of the major aspects to be considered in Arabic language processing. In this paper, we present an approach for developing a numeral checker application for the Arabic language, called "Arabic Numerals Checker". Arabic Numerals Checker is intended to help users achieve competence in writing according to the Arabic numeral rules. It is supported by an explanation facility that provides the user with a clear explanation of the output. The use of the Arabic Numerals Checker, when integrated with other language processing applications, can enhance the quality of the text for users who read or write in the Arabic language. The Arabic Numerals Checker is implemented as a simple Finite State Transducer that maps numbers to the corresponding numerals in a way that respects the gender agreement feature. The proposed approach covers all the numbers in the range 1 to 999,999,999,999. Tests have been executed using random numbers in the range above. Experimental results have demonstrated that the proposed approach is promising in the sense that it provides efficient and error-free transformation.

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.