In this paper, we address the problems of Arabic Text Classification and stemming using Transducers and Rational Kernels. We introduce a new stemming technique based on the use of Arabic patterns (Pattern Based Stemmer). Patterns are modelled using transducers and stemming is done without depending on any dictionary. Using transducers for stemming, documents are transformed into finite state tr...

Bidirectional Encoder Representations from Transformers (BERT) has gained increasing attention researchers and practitioners as it proven to be an invaluable technique in natural languages processing. This is mainly due its unique features, including ability predict words conditioned on both the left right context, pretrained using plain text corpus that enormously available web. As BERT more i...

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...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their comb...

Text classification is a supervised technique that uses labelled training data to learn the classification system and then automatically classifies the remaining text using the learned system. This paper investigates Naïve Bayesian algorithm based on Chi Square features selection method. The base of our comparisons are macro F1, macro recall and macro precision evaluation measures. The experime...

This research is conducted in order to compare the performance of three known text classification techniques namely, Support Vector Machine (SVM) classifier, Naïve Bayes (NB) classifier, and C4.5 Classifier. Text classification aims to automatically assign the text to a predefined category based on linguistic features, and content. These three techniques are compared using a set of Arabic text ...