In this paper, we propose a new feature-selection algorithm for text classification, called best
terms (BT). The complexity of BT is linear in respect to the number of the training-set …

We live in the world where information have a great value and the amount of available
information (mostly on internet) has been expansively growing during last years. There are …

Most text categorization algorithms represent a document collection as a Bag of Words
(BOW). The BOW representation is unable to recognize synonyms from a given term set and …

Traditionally, text classifiers are built from labeled training examples. Labeling is usually
done manually by human experts (or the users), which is a labor intensive and time …

Text classification is becoming more and more important with the rapid growth of on-line
information available. This paper describes the text classification process. Of course, a …

Automated text classification has been considered as a vital method to manage and process
a vast amount of documents in digital forms that are widespread and continuously …

Most of the text categorization algorithms in the literature represent documents as collections
of words. An alternative which has not been sufficiently explored is the use of word …

The way in which text is represented has a strong impact on the performance of text
classification (retrieval and categorization) systems. We discuss the operation of text …

The bag-of-words approach to text document representation typically results in vectors of the
order of 5000–20,000 components as the representation of documents. To make effective …