Recently the rate of written colloquial text has increased dramatically. It is being used as a medium of expressing ideas especially across the WWW, usually in the form of blogs and partially colloquial articles. Most of these written colloquial has been in the Egyptian colloquial dialect, which is considered the most widely dialect understood and used throughout the Arab world. Modern Standard...

Research done on Arabic sentiment analysis is considered very limited almost in its early steps compared to other languages like English whether at document-level or sentence-level. In this paper, we test the effect of preprocessing (normalization, stemming, and stop words removal) on the performance of an Arabic sentiment analysis system using Arabic tweets from twitter. The sentiment (positiv...

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...

As a continuation of our efforts towards tackling the problem of spoken Dialect Identification (DID) for Arabic languages, we present the QCRI-MIT Advanced Dialect Identification System (QMDIS). QMDIS is an automatic spoken DID system for Dialectal Arabic (DA). In this paper, we report a comprehensive study of the three main components used in the spoken DID task: phonotactic, lexical and acous...

The paper presents work on improved sentence-level dialect classification of Egyptian Arabic (ARZ) vs. Modern Standard Arabic (MSA). Our approach is based on binary feature functions that can be implemented with a minimal amount of task-specific knowledge. We train a featurerich linear classifier based on a linear support-vector machine (linear SVM) approach. Our best system achieves an accurac...

The daily spoken variety of Arabic is often termed the colloquial or dialect form of Arabic. There are many Arabic dialects across the Arab World and within other Arabic speaking communities. These dialects vary widely from region to region and to a lesser extent from city to city in each region. The dialects are not standardized, they are not taught, and they do not have official status. Howev...

This paper presents the systems submitted by the MAZA team to the Arabic Dialect Identification (ADI) shared task at the VarDial Evaluation Campaign 2017. The goal of the task is to evaluate computational models to identify the dialect of Arabic utterances using both audio and text transcriptions. The ADI shared task dataset included Modern Standard Arabic (MSA) and four Arabic dialects: Egypti...

Natural language processing technology for the dialects of Arabic is still in its infancy, due to the problem of obtaining large amounts of text data for spoken Arabic. In this paper we describe the development of a part-of-speech (POS) tagger for Egyptian Colloquial Arabic. We adopt a minimally supervised approach that only requires raw text data from several varieties of Arabic and a morpholo...