We present a study on sentence-level Arabic Dialect Identification using the newly developed Multidialectal Parallel Corpus of Arabic (MPCA) – the first experiments on such data. Using a set of surface features based on characters and words, we conduct three experiments with a linear Support Vector Machine classifier and a meta-classifier using stacked generalization – a method not previously a...

We present OSMAN (Open Source Metric for Measuring Arabic Narratives) a novel open source Arabic readability metric and tool. It allows researchers to calculate readability for Arabic text with and without diacritics. OSMAN is a modified version of the conventional readability formulas such as Flesch and Fog. In our work we introduce a novel approach towards counting short, long and stress syll...

Natural language processing technology for the dialects of Arabic is still in its infancy, due to the problem of obtaining large amounts of text data for spoken Arabic. In this paper we describe the development of a part-of-speech (POS) tagger for Egyptian Colloquial Arabic. We adopt a minimally supervised approach that only requires raw text data from several varieties of Arabic and a morpholo...

Off-line recognition of text plays a significant role in several applications such as the automatic sorting of postal mail or editing old documents. The recognition of Arabic handwriting characters is a difficult task owing to the similar appearance of some different characters. Most researchers have presented methods that recognise isolated characters. However, recognition of all shapes of Ara...

Modern standard Arabic is usually written without diacritics. This makes it difficult for performing Arabic text processing. Diacritization helps clarify the meaning of words and disambiguate any vague spellings or pronunciations, as some Arabic words are spelled the same but differ in meaning. In this paper, we address the issue of adding diacritics to undiacritized Arabic text using a hybrid ...

text classification is an important research field in information retrieval and text mining. the main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. since word detection is a difficult and time consuming task in persian language, bayesian text classifier is an appropriate approach to deal with different...

This paper provides a novel model for English/Arabic Query Translation to search Arabic text, and then expands the Arabic query to handle Arabic OCR-Degraded Text. This includes detection and translation of word collocations, translating single words, transliterating names, and disambiguating translation and transliteration through different approaches. It also expands the query with the expect...

Genuine numerical multilingual text classification is almost impossible if only words are treated as the privileged unit of information. Although text tokenization (in which words are considered as tokens) is relatively easy in English or French, it is much more difficult for other languages such as German or Arabic. Moreover, stemming, typically used to normalize and reduce the size of the lex...