Arabic natural language processing: An overview

Abstract

Arabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script (Arabizi), which corresponds to Arabic written with Latin letters, numerals and punctuation. Due to the complexity of this language and the number of corresponding challenges for NLP, many surveys have been conducted, in order to synthesise the work done on Arabic. However these surveys principally focus on two varieties of Arabic (MSA and AD, written in Arabic letters only), they are slightly old (no such survey since 2015) and therefore do not cover recent resources and tools. To bridge the gap, we propose a survey focusing on 90 recent research papers (74% of which were published after 2015). Our study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.

References in corpus (9)

• Distributed Representations of Words and Phrases and their Compositionality
• Neural Machine Translation by Jointly Learning to Align and Translate
• Distributed Representations of Sentences and Documents
• Character-Aware Neural Language Models
• Sequence-to-sequence neural network models for transliteration
• Shamela: A Large-Scale Historical Arabic Corpus
• Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition
• An Arabic Dependency Treebank in the Travel Domain
• MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction

Cited by in corpus (3)

• ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
• Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English
• Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT