In this paper we present the final result of a project on Tunisian Arabic
encoded in Arabizi, the Latin-based writing system for digital conversations.
The project led to the creation of two integrated and independent resources: a
corpus and a NLP tool created to annotate the former with various levels of
linguistic information: word classification, transliteration, tokenization,
POS-tagging, lemmatization. We discuss our choices in terms of computational
and linguistic methodology and the strategies adopted to improve our results.
We report on the experiments performed in order to outline our research path.
Finally, we explain why we believe in the potential of these resources for both
computational and linguistic researches. Keywords: Tunisian Arabizi, Annotated
Corpus, Neural Network Architecture

本研究在于使用阿拉伯语拉丁写法（Arabizi）对突尼斯阿拉伯语进行编码，在此基础上创建了一个包含语料库和 NLP 工具的资源，用于提供各级语言信息的词语分类、音译、标记、词形归总等，并讨论了使用计算和语言方法进行研究路径和实验的策略等问题，最终提供了可能广泛用于计算和语言研究的资源。

TArC: 突尼斯阿拉伯语语料库的首个完整版本发布

TArC: Tunisian Arabish Corpus First complete release

On social media, Arabic people tend to express themselves in their own local
dialects. More particularly, Tunisians use the informal way called "Tunisian
Arabizi". Analytical studies seek to explore and recognize online opinions
aiming to exploit them for planning and prediction purposes such as measuring
the customer satisfaction and establishing sales and marketing strategies.
However, analytical studies based on Deep Learning are data hungry. On the
other hand, African languages and dialects are considered low resource
languages. For instance, to the best of our knowledge, no annotated Tunisian
Arabizi dataset exists. In this paper, we introduce TUNIZI a sentiment analysis
Tunisian Arabizi Dataset, collected from social networks, preprocessed for
analytical studies and annotated manually by Tunisian native speakers.

本研究介绍了 TUNIZI，一种情感分析突尼斯语阿拉伯字母表数据集，旨在开发和提高分析研究，通过社交媒体收集并由突尼斯本土讲话者手动标注准备。