Text categorization or text classification (TC) has recently received increased research
attention from information retrieval and machine learning communities, this focus is driven …

The exponential growth of the internet has led to a great deal of interest in developing useful
and efficient tools and software to assist users in searching the Web. Document retrieval …

We present a simple approach for the classification of “noisy” documents using bigrams and
named entities. The approach combines conventional feature selection with a contextual …

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 …

In this note, we present results concerning the theory and practice of determining for a given
document which of several categories it best fits. We describe a mathematical model of …

This thesis will discuss feature selection algorithms for text-categorization. Feature selection
algorithms are very important, as they can make-or-break a categorization engine. The …

this paper contains an overview of basic formulations and approaches to text classification.
This paper surveys the algorithms used in text categorization: handcrafted rules, decision …

Most text classification techniques assume that manually labeled documents (corpora) can
be easily obtained while learning text classifiers. However, labeled training documents are …

Text categorization–the assignment of natural language documents to one or more
predefined categories based on their semantic content–is an important component in many …