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 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...

In Cross Language Information Retrieval (CLIR), queries in one language retrieve documents in other language(s). This can be done through Query Translation that comes up against Translation/Transliteration challenges like ambiguity as the main problems. In this paper, a comprehensive solution has been introduced for these challenges. 1, 4 powerful English/Arabic Machine Readable Dictionaries (M...

This paper is about improving the quality of Arabic-English statistical machine translation (SMT) on dialectal Arabic text using morphological knowledge. We present a light-weight rule-based approach to producing Modern Standard Arabic (MSA) paraphrases of dialectal Arabic out-of-vocabulary (OOV) words and low frequency words. Our approach extends an existing MSA analyzer with a small number of...

Vocalization, diactritization or diacritics restoration is one of the major challenges in Arabic natural language processing. Algiers dialect is also concerned by this issue. In this paper, we present an automatic diacritization system for standard and dialect Arabic texts based on statistical approach. The idea is to use available tools in statistical machine translation to build such a system...

The present paper describes the experience gained at LDC in the collection and transcription of conversational dialectal Arabic. The paper will cover the following: (a) Arabic language background; (b) objectives. principles, and methodological choices of dialectal Arabic transcription, (c) design features of LDC‟s „Arabic MultiDialectal Transcription Tool‟ (AMADAT) and metalanguage transcriptio...

The Named Entity Recognition (NER) is a task in Information Extraction (IE). The Named entity recognition has become very important for natural language processing. The named entity recognition is defined as the detection and classification of entities from un-structured text where for the Arabic language, the named entity recognition is new in the natural language processing although it has pr...

The Linguistic Data Consortium and Georgetown University Press are collaborating to create updated editions of bilingual dictionaries that had originally been published in the 1960’s for English-speaking learners of Moroccan, Syrian and Iraqi Arabic. In their first editions, these dictionaries used ad hoc Latin-alphabet orthography for each colloquial Arabic dialect, but adopted some properties...

Part-of-Speech (POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because there are many phenomena that frequently appear in Twitter that are not as common, or are entirely absent, in other domains: tweets are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengt...

In 1947, Egyptian novelist, short story writer and playwright Mahmud Taymur (1894-1973) was awarded the first prize in short story writing from Cairo Arabic Language Academy. Two years later, Taymur was made a member of the prestigious scholarly institution whose main mission was to promote and defend the use of classical Arabic in modern Arabic literature. In both the statement made by the jur...