We approximate Arabic’s rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. The algorithm uses ...

An algorithm and a system for searching handwritten Arabic documents to locate key words is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary lookup, locates word images in an indexed (segmented) ...

The paper presents a rapid method of developing a shallow Arabic morphological analyzer. The analyzer will only be concerned with generating the possible roots of any given Arabic word. The analyzer is based on automatically derived rules and statistics. For evaluation, the analyzer is compared to a commercially available Arabic Morphological Analyzer.

Study Design: A prospective 1-year follow-up study of whiplash patients presenting with neurological signs/or symptoms (WADIII), and whiplash patients with neck pain but no neurologic findings (WADI/II). Objective: We hypothesize that WADI/II and WADIII are distinct entities, with regards to clinical presentation, pathoanatomy, and prognosis. Summary of Background Data: symptoms associated with...

The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an inde...

Despite of the decent development of the pattern recognition science applications in the last decade of the twentieth century and this century, text recognition remains one of the most important problems in pattern recognition. To the best of our knowledge, little work has been done in the area of Arabic text recognition compared with those for Latin, Chins and Japanese text. The main difficult...

The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural languag...

Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of...