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)
This paper introduces a large-scale dual-mode stochastic system to automatically diacritize raw Arabic text. The first of these modes determines the most likely diacritics by choosing the sequence of full-form Arabic word diacritizations with maximum marginal probability via A^ lattice search and long-horizon n-grams probability estimation. When full-form(More)
In this paper, we present the latest version of our system for identifying linguistic code switching in Arabic text. The system relies on Language Models and a tool for morphological analysis and disambiguation for Arabic to identify the class of each word in a given sentence. We evaluate the performance of our system on the test datasets of the shared task(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)
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 Dialectal Arabic (EDA) or some other class and whether the whole sentence is mostly EDA or MSA. The token level(More)
In this paper, we describe our Hybrid Arabic Spelling and Punctuation Corrector (HASP). HASP was one of the systems participating in the QALB-2014 Shared Task on Arabic Error Correction. The system uses a CRF (Conditional Random Fields) classifier for correcting punctuation errors, an open-source dictionary (or word list) for detecting errors and generating(More)
This paper introduces an Arabic text annotation tool called Fassieh<sup>reg</sup>. Via a sophisticated interactive GUI application, Fassieh<sup>reg</sup> makes it easy to build structured large standard written Arabic corpora, then allows the production of fundamental linguistic analyses; i.e., language factorizations, at high coverage and accuracy rates(More)
We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses the word length, character level (1, 2, 3, 4, and 5)-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We test our proposed framework and compare it(More)