This contribution describes an Arabic-English parallel word aligned treebank corpus from the Linguistic Data Consortium that is currently under production. Herein we primarily focus on efforts required to assemble the package and instructions for using it. It was crucial that word alignment be performed on tokens produced during treebanking to ensure cohesion and greater utility of the corpus. ...

This paper presents a novel approach for Arabic root generation and lexicon development. The approach provides three algorithms; in the first algorithm Arabic word root is generated using the concept of permutation and combination, the root generator algorithm generates roots by applying permutations to the Arabic alphabetic letters. Then, the second algorithm is used for developing difference ...

This paper presents a hybrid approach to the enhancement of English to Arabic statistical machine translation quality. Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another. Arabic, as a morphologically rich language, is a highly flexional language, in that the same root can lead to various forms according to i...

complies with the regulations of the University and meets the accepted standards with respect to originality and quality. Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. Several approaches of word segmentation have been proposed for Latin-based languages while a few of them have been introduced for...

Character recognition for Arabic texts poses a twofold challenge, segmenting words into letters and identifying the individual letters. We propose a method that combines the two tasks, using a grid of SIFT descriptors as features for classification of letters. Each word is scanned with increasing window sizes; segmentation points are set where the classifier achieves maximal confidence. Using t...

Hidden Markov Models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a Model discriminant HMM is presented. A complete system able to classify Arabic-Handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove s...

Word recognition systems use a lexicon to guide the recognition process in order to improve the recognition rate. However, as the lexicon grows, the computation time increases. In this paper, we present the Arabic word descriptor (AWD) for Arabic word shape indexing and lexicon reduction in handwritten documents. It is formed in two stages. First, the structural descriptor (SD) is computed for ...

The Arabic language has a very rich morphology where a word is composed of zero or more prefixes, a stem and zero or more suffixes. This makes Arabic data sparse compared to other languages, such as English, and consequently word segmentation becomes very important for many Natural Language Processing tasks that deal with the Arabic language. We present in this paper two segmentation schemes th...

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of...

Part Of Speech (POS) tagging forms the important preprocessing step in many of the natural language processing applications such as text summarization, question answering and information retrieval system. It is the process of classifying every word in a given context to its appropriate part of speech. Different POS tagging techniques in the literature have been developed and experimented. Curre...