Arabic Text Categorization using Machine Learning Approaches

Arabic Text Categorization using Machine Learning Approaches

Arabic Text categorization is considered one of the severe problems in classification using machine learning algorithms. Achieving high accuracy in Arabic text categorization depends on the preprocessing techniques used to prepare the data set. Thus, in this paper, an investigation of the impact of the preprocessing methods concerning the performance of three machine learning algorithms, namely...

Machine learning for Arabic text categorization

Arabic Text Categorization Using Classification Rule Mining

Text categorization is one of the known problems in classification data mining. It aims to mapping text documents into one or more predefined class or category based on its contents of keywords. This problem has recently attracted many scholars in the data mining and machine learning communities since the numbers of online documents that hold useful information for decision makers, are numerous...

Arabic Text Categorization

In this paper, we compare the performance of three classifiers for Arabic text categorization. In particular, the naïve Bayes, k-nearest-neighbors (knn), and distance-based classifiers were used. Unclassified documents were preprocessed by removing punctuation marks and stopwords. Each document is then represented as a vector of words (or of words and their frequencies as in the case of the naï...

Using "Annotator Rationales" to Improve Machine Learning for Text Categorization

We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting this annotation—thereby teaching the machine learner why the example belongs to the category. We provid...