Tokenization of Tunisian Arabic: A Comparison between Three Machine Learning Models

Abstract

Tokenization represents the way of segmenting a piece of text into smaller units called tokens. Since Arabic is an agglutinating language by nature, this treatment becomes a crucial preprocessing step for many Natural Language Processing (NLP) applications such as morphological analysis, parsing, machine translation, information extraction, and so on. In this article, we investigate word tokenization task with a rewriting process to rewrite the orthography of the stem. For this task, we are using Tunisian Arabic (TA) text. To the best of the researchers' knowledge, this is the first study that uses TA for word tokenization. Therefore, we start by collecting and preparing various TA corpora from different sources. Then, we present a comparison of three character-based tokenizers based on Conditional Random Fields (CRF), Support Vector Machines (SVM) and Deep Neural Networks (DNN). The best proposed model using CRF achieved an F-measure result of 88.9%.