We present a method for generating Colloquial Egyptian Arabic (CEA) from morphologically disambiguated Modern Standard Arabic (MSA). When used in POS tagging, this process improves the accuracy from 73.24% to 86.84% on unseen CEA text, and reduces the percentage of out-ofvocabulary words from 28.98% to 16.66%. The process holds promise for any NLP task targeting the dialectal varieties of Arabi...

Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substa...

The report addresses the basic problems of the Arabic language formalization based on analysis of linguistic errors in software products. Reviewing the principles of modern information systems operation the authors come to the conclusion that the existing methods of the Arabic formalization allow to note a shift towards the technological aspects of the linguistic processing of facts, however, t...

This paper provides a new model enhancing the Arabic OCR degraded text retrieval effectiveness. The proposed model based on simulating the Arabic OCR recognition mistakes on a word based approach. Then the model expands the user search query using the expected OCR errors. The resulting expanded search query gives higher precision and recall in searching Arabic OCR-Degraded text rather than the ...

Handwriting is considered to be one of the commonly used modality to identify persons in commercial, governmental and forensic applications. In order to record recent advances in the field of writer identification, we are proposing to organize the ICDAR2015 writer identification competition using KHATT, AHTID/MW and IBHC Databases. A first edition of the Arabic Writer Identification Competition...

Despite of much work on TTS technologies and several TTS systems customized for Korean, current TTS systems output many errors in transliterating non-alphabetic symbols such as Arabic numerals and text symbols. This paper proposes TLAN (Transliteration Learner for Arabic-Numeral expressions) which can efficiently disambiguate the reading and meaning of Arabic Numeral Expressions (ANEs) in texts...

This paper focuses on Automatic Arabic classifications. Arabic language is highly inflectional and derivational language which makes text mining a complex task. In classifying Arabic text, there are many published experimental results. Since these results came from different datasets, authors and evaluation metrics, we cannot compare the performance of the experimented classifiers. In this pape...

Clitics in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic language. We tokenize large Arabic text using white-spaces and an automatic clitics tokenizer (AMIRA 2.0) and compare the unique-word count in both cases with English language. We also show the re...

We demonstrate a data collection and analysis system that can be used to analyze the relative contributions of dialect dependent variation in the lexical of speech-like Arabic text. We utilize Latent Dirichlet Allocation (LDA), a generative Probabilistic modeling method, to analyze a phonetic Latin Spelled Arabic online chat corpus. The corpus produces different word choices and word relations ...

In this paper, a novel for Query Translation and Expansion for enabling English/Arabic CLIR for both normal and OCR-Degraded Arabic Text model has been proposed, implemented, and tested. First, an English/Arabic Word Collocations Dictionary has been established plus reproducing three English/Arabic Single Words Dictionaries. Second, a modern Arabic Corpus has been built. Third, a model for simu...