In this paper, a novel Arabic text categorization system has been developed based on statistical learning. The system uses a new method for feature extraction. The system has been implemented and tested using an Arabic text corpus. Results prove that the efficiency of the proposed system in text categorization of Arabic documents. Moreover, the system proved powerfulness in grasping the semanti...

In this paper, we focus on a sub-problem of Arabic text error correction, namely Arabic Text Denormalization. Text Denormalization is considered an important post-processing step when performing machine translation into Arabic. We examine different approaches for denormalization via the use of language modeling, stemming, and sequence labeling. We show the effectiveness of different approaches ...

The paper presents a research in Arabic Information Retrieval (IR). It surveys the impact of statistical and morphological analysis of Arabic text in improving Arabic IR relevancy. We investigated the contributions of Stemming, Indexing, Query Expansion, Text Summarization (TS), Text Translation, and Named Entity Recognition (NER) in enhancing the relevancy of Arabic IR. Our survey emphasizing ...

The Named Entity Recognition (NER) is a task in Information Extraction (IE). The Named entity recognition has become very important for natural language processing. The named entity recognition is defined as the detection and classification of entities from un-structured text where for the Arabic language, the named entity recognition is new in the natural language processing although it has pr...

This research presents and compares the impact of text preprocessing, which has not been addressed before, on Arabic text classification using popular text classification algorithms; Decision Tree, K Nearest Neighbors, Support Vector Machines, Naïve Bayes and its variations. Text preprocessing includes applying different term weighting schemes, and Arabic morphological analysis (stemming and li...

Sentiment analysis research has predominantly been on English texts. Thus there exist many sentiment resources for English, but less so for other languages. Approaches to improve sentiment analysis in a resource-poor focus language include: (a) translate the focus language text into a resource-rich language such as English, and apply a powerful English sentiment analysis system on the text, and...

Computer information and retrieval is becoming increasingly sophisticated and is being exploited in more and more spheres of human activity. Many computer applications are developed as information distribution systems, of which the Internet is one of the best known and widely used. With enormous quantities of data in different languages available on the net, it is essential that more efficient ...

The using of the internet with its technologies and applications have been increased rapidly. So, protecting the text from illegal use is too needed . Text watermarking is used for this purpose. Arabic text has many characteristics such existing of diacritics , kashida (extension character) and points above or under its letters .Each of Arabic letters can take different shapes with different Un...

This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author t...

We present our correction annotation guidelines to create a manually corrected nonnative (L2) Arabic corpus. We develop our approach by extending an L1 large-scale Arabic corpus and its manual corrections, to include manually corrected non-native Arabic learner essays. Our overarching goal is to use the annotated corpus to develop components for automatic detection and correction of language er...