The written form of Arabic, Modern Standard Arabic (MSA), differs quite a bit from the spoken dialects of Arabic, which are the true “native” languages of Arabic speakers used in daily life. However, due to MSA’s prevalence in written form, almost all Arabic datasets have predominantly MSA content. We present the Arabic Online Commentary Dataset, a 52M-word monolingual dataset rich in dialectal...

This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a; Al-Sabbagh and Girju, 2012). This wor...

Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. DA lives side-by-side with the official language, Modern Standard Arabic (MSA). DA differs from MSA on all levels of linguistic representation, from phonology and morphology to lexicon and syntax. Unlike MSA, DA has no standard orthography since there are no Arabic dialect academies, nor is there a large edited...

A major challenge for statistical machine translation (SMT) of Arabic-to-English user-generated text is the prevalence of text written in Arabizi, or Romanized Arabic. When facing such texts, a translation system trained on conventional Arabic-English data will suffer from extremely low model coverage. In addition, Arabizi is not regulated by any official standardization and therefore highly am...

In this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic differ significantly. Different mono-lingual and multi-lingual hybrid SMT approaches are compared. Mono-lingual systems do always result in better translation accuracy in one Arabic fo...

Part-of-Speech (POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because they are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengths. Arabic tweets also show a further range of linguistic phenomena such as usage of different dialects, romanised Arabic and b...

Parsing of Arabic sentences is a necessary mechanism for many natural language processing applications such as machine translation; question answering, knowledge extraction and information retrieval. In this study, we present a top-down chart parser for parsing simple Arabic sentences, including nominal and verbal sentences within specific domain Arabic grammar. We used the Context Free Grammar...

This paper investigates the unique pharyngeal and uvular consonants of Arabic from the automatic speech recognition (ASR) point of view. Comparisons of the recognition error rates for these phonemes are analyzed in five experiments that involve different combinations of native and non-native Arabic speakers. The most three confusing consonants for every investigated consonant are uncovered and ...

The objective of this paper is to present an under-resourced language related to Arabic. In fact, in several countries through the Arabic world, no one speaks the modern standard Arabic language. People speak something which is inspired from Arabic but could be very different from the modern standard Arabic. This one is reserved for the official broadcast news, official discourses and so on. Th...

Although, optical character recognition has made tremendous achievements in the area of desktop publishing, yet a huge amount of work is required to be done. Unlike Roman like languages, there are various languages possessing a large number of fonts and/or having complicated shapes. Arabic language is one of those languages, which is somewhat complicated in its construction. Although a reasonab...