In the Arab world, while Modern Standard Arabic is commonly used in formal written context, on sites like Youtube, people are increasingly using Dialectal Arabic, the language for everyday use to comment on a video and interact with the community. These user-contributed comments along with the video and user attributes, offer a rich source of multi-dialectal Arabic sentences and expressions fro...

In this article we present Curras, the first morphologically annotated corpus of the Palestinian Arabic dialect. Palestinian Arabic is one of the many primarily spoken dialects of the Arabic language. Arabic dialects are generally under-resourced compared to Modern Standard Arabic, the primarily written and official form of Arabic. We start in the article with a background description that situ...

Tunisian Arabic is a dialect of the Arabic language spoken in Tunisia. Tunisian Arabic is an under-resourced language. It has neither a standard orthography nor large collections of written text and dictionaries. Actually, there is no strict separation between Modern Standard Arabic, the official language of the government, media and education, and Tunisian Arabic; the two exist on a continuum ...

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 current status. The lexical entries are augm...

The Arabic language is a collection of multiple variants, among which Modern Standard Arabic (MSA) has a special status as the formal written standard language of the media, culture and education across the Arab world. The other variants are informal spoken dialects that are the media of communication for daily life. Arabic dialects differ substantially from MSA and each other in terms of phono...

The paper describes the QCRI submissions to the shared task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African (Maghrebi), and Modern Standard Arabic (MSA). The relatively small training set is automatically generated from an ASR system. To avoid over-fitting on such small data, we selected and designed features that capture the mo...

This paper introduces a dual-mode stochastic system to automatically identify linguistic code switch points in Arabic. The first of these modes determines the most likely word tag (i.e. dialect or modern standard Arabic) by choosing the sequence of Arabic word tags with maximum marginal probability via lattice search and 5-gram probability estimation. When words are out of vocabulary, the syste...

Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, howe...

PURPOSE The purpose of this study was to investigate how linguistic knowledge interacts with indexical knowledge in older children's perception under demanding listening conditions created by extensive talker variability. METHOD Twenty-five 9- to 12-year-old children, 12 from North Carolina (NC) and 13 from Wisconsin (WI), identified 12 vowels in isolated /hVd/ words produced by 120 talkers rep...

This report describes a pilot study investigating whether adult listeners are able to identify the dialect of child speakers from the recordings of isolated words. The recorded children are either speaking a Stockholm dialect or a Scanian dialect. Despite low agreement between the nine adult listeners, the results show that adult listeners can indeed identify dialect even in isolated short word...