In this notebook, we describe our approach to cope with the Author Profiling task on PAN17 which consists of both gender and language identification for Twitter’s users. We used our MicroTC (μTC) framework as the primary tool to create our classifiers. μTC follows a simple approach to text classification; it converts the problem of text classification to a model selection problem using several ...

This paper is a quick review of some of the scholarly work aiming at solving various problems of the Arabic language using neural networks. It includes some research work concerning online recognition of handwritten Arabic characters, speech recognition, offline character text recognition, text categorization and recognition of printed text. This paper concludes that more research should be con...

In this paper we present a survey of the literature on Arabic writer identification scheme and up-to date techniques employed in identification. The paper begins with an overview of the various writer identification schemes in Arabic and Persian languages. After that, an attempt is made to describe the complex character of Arabic strokes. Previous studies have used a number of Arabic datasets c...

The importance of building sentiment analysis tools for Arabic social media has been recognized during the past couple of years, especially with the rapid increase in the number of Arabic social media users. One of the main difficulties in tackling this problem is that text within social media is mostly colloquial, with many dialects being used within social media platforms. In this paper, we p...

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting ...

In this paper we present a system for document understanding and for recognition of printed Arabic text. Arabic characters must be segmented before recognition. We overcome the problem of segmentation by our proposed ORAN system (Offline Recognition of Arabic characters and Numerals). ORAN is based on a method called Modified MCR. Using a stroke index, we can parse compound document images into...

This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by ...

Optical character recognition (OCR) systems provide human-machine interaction and are widely used in many applications. Much research has already been done on the recognition of Latin, Chinese and Japanese characters. Against this background, it has been experienced that only few papers have specifically addressed to the problem of Arabic text recognition and languages using Arabic script like ...