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.

该研究对阿拉伯语的三个主要变体（MSA、AD 和 CA），特别是由拉丁字母和数字组成的 Arabizi 形式的文字，在阿拉伯文本处理中的挑战以及可用资源和工具方面进行了调查，并对最近研究论文进行了分类和总结。

阿拉伯语自然语言处理概述

Arabic natural language processing: An overview

Data annotation is an important but time-consuming and costly procedure. To
sort a text into two classes, the very first thing we need is a good annotation
guideline, establishing what is required to qualify for each class. In the
literature, the difficulties associated with an appropriate data annotation has
been underestimated. In this paper, we present a novel approach to
automatically construct an annotated sentiment corpus for Algerian dialect (a
Maghrebi Arabic dialect). The construction of this corpus is based on an
Algerian sentiment lexicon that is also constructed automatically. The
presented work deals with the two widely used scripts on Arabic social media:
Arabic and Arabizi. The proposed approach automatically constructs a sentiment
corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to
Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi
test sets, respectively. Ongoing work is aimed at integrating transliteration
process for Arabizi messages to further improve the obtained results.

本文提出了一种自动构建阿尔及利亚方言情感语料库的新方法，该语料库基于自动构建的阿尔及利亚情感词典，涉及使用阿拉伯文和阿拉伯字母拼音写成的社交媒体数据，并且取得了较好的实验结果。

SentiALG：阿尔及利亚情感分析自动语料标注

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Arabizi is Arabic text that is written using Latin characters. Arabizi is
used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is
commonly used in informal settings such as social networking sites and is often
with mixed with English. In this paper we address the problems of: identifying
Arabizi in text and converting it to Arabic characters. We used word and
sequence-level features to identify Arabizi that is mixed with English. We
achieved an identification accuracy of 98.5%. As for conversion, we used
transliteration mining with language modeling to generate equivalent Arabic
text. We achieved 88.7% conversion accuracy, with roughly a third of errors
being spelling and morphological variants of the forms in ground truth.

本文描述了如何使用单词和序列级别的特征来识别英语混杂的 Arabizi 文字，并使用语言建模进行转译，在保真度上达到了 88.7％的识别精度。