In this paper, we compare several aspects related to automatic text categorization which
include document representation, feature selection, three classifiers, and their application to …

Feature selection is an important research issue in text categorization. The reason for this is
that thousands of features are often involved, even when the simplest document …

This paper deals with automatic supervised classification of documents. The approach
suggested is based on a vector representation of the documents centred not on the words …

In text classification most techniques use bag-of-words to represent documents. The main
problem is to identify what words are best suited to classify the documents in such a way as …

Automatic text classification is the process of automatically classifying text documents into
pre-defined document classes. Traditionally, documents are represented in the so called …

Text Categorization, which consists of automatically assigning documents to a set of
categories, usually involves the management of a huge number of features. Most of them are …

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 …

Text Categorisation (TC) consists of automatically assigning documents to a set of prefixed
categories. It usually involves the management of a huge number of features. Some of them …

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 …

A major difficulty of text categorization problems is the high dimensionality of the feature
space. Thus, feature selection is often performed in order to increase both the efficiency and …