Term-based representations of documents have found wide-spread use in information
retrieval. However, one of the main shortcomings of such methods is that they largely …

Text classification aims at mapping documents into a set of predefined categories.
Supervised machine learning models have shown great success in this area but they …

Current text classification systems typically use term stems for representing document
content. Semantic Web technologies allow the usage of features on a higher semantic level …

Previous researches on advanced representations for document retrieval have shown that
statistical state-of-the-art models are not improved by a variety of different linguistic …

This paper takes a fresh look at an old idea in Information Retrieval: the use of linguistically
extracted phrases as terms in the automatic categorization (aka classification) of documents …

We describe the results of extensive machine learning experiments on large collections of
Reuters' English and German newswires. The goal of these experiments was to …

Accurate semantic representation models are essential in text mining applications. For a
successful application of the text mining process, the text representation adopted must keep …

We investigate a meta-model approach, called Meta-learning Using Document Feature
characteristics (MUDOF), for the task of automatic textual document categorization. It …

Abstract Automatic Text Categorisation (TC) is a complex and useful task for many natural
language applications, and is usually performed through the use of a set of manually …

In text categorization, term selection is an important step for the sake of both categorization
accuracy and computational efficiency. Different dimensionalities are expected under …