Mohamed Al-Badrashiny

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In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007). MADAMIRA improves upon the two systems with a more(More)
—This paper presents a stochastic-based approach for misspelling correction of Arabic text. In this approach, a context-based two-layer system is utilized to automatically correct misspelled words in large datasets. The first layer produces a list in which possible alternatives for each misspelled word are ranked using the Damerau–Levenshtein edit distance.(More)
In this paper, we address the problem of converting Dialectal Arabic (DA) text that is written in the Latin script (called Arabizi) into Arabic script following the CODA convention for DA orthography. The presented system uses a finite state transducer trained at the character level to generate all possible transliterations for the input Arabizi words. We(More)
We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe Tharwa's creation process and report on its(More)
Applications of statistical Arabic NLP in general, and text mining in specific, along with the tools underneath perform much better as the statistical processing operates on deeper language factorization(s) than on raw text. Lexical semantic factorization is very important in that aspect due to its feasibility, high level of abstraction, and the language(More)
In this paper, we present a hybrid approach for performing token and sentence levels Dialect Identification in Arabic. Specifically we try to identify whether each token in a given sentence belongs to Modern Standard Arabic (MSA), Egyptian Dialec-tal Arabic (EDA) or some other class and whether the whole sentence is mostly EDA or MSA. The token level(More)
DIRA is a query expansion tool that generates search terms in Standard Arabic and/or its dialects when provided with queries in English or Standard Arabic. The retrieval of dialectal Arabic text has recently become necessary due to the increase of dialectal content on social media. DIRA addresses the challenges of retrieving information in Arabic dialects,(More)