We describe several systems for identifying short samples of Arabic or SwissGerman dialects, which were prepared for the shared task of the 2017 DSLWorkshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The SwissGerman data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63% on both the Swiss-German and A...

Objective: The main objective of this work is to build a comprehensive benchmarking database of online Arabic text. Part of this objective is the development of tools, techniques and procedures for online text collection, verification and transliteration. Additionally, we built a dataset for segmented online Arabic characters and ligatures with ground truth labeling and present classification r...

Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied a...

We describe a SMS-based information system called CATS, which allows posting and searching through free Arabic text using Information Extraction (IE) technology. We discuss the challenges of applying IE technology for unedited real Arabic text. In addition, we describe the structure of this system and our approach to produce an open robust system capable of including more sub domains with the m...

We present a free, Java-based library named “AraNLP” that covers various Arabic text preprocessing tools. Although a good number of tools for processing Arabic text already exist, integration and compatibility problems continually occur. AraNLP is an attempt to gather most of the vital Arabic text preprocessing tools into one library that can be accessed easily by integrating or accurately adap...

Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. In this paper, we investigate Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popula...

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition syste...

The contribution of this work relates to the field of Arabic text-based document analysis for the detection of plagiarism. This analysis will be carried out according to the triadic computation model of document similarity. The authors propose a hybrid segmentation prototype for Arabic text-based documents that links different processing steps in order to generate the similarity rate between th...

One of the issues that arise in the text steganography is the capacity of hiding secret bit. Focusing in Arabic text steganography we propose a sharp-edges method to encounter the issue. This new method will hide the secret bits in the sharp-edges for each character in the Arabic text document. The main processes involved are identifying sharp-edges in the cover-text, secret message preparation...