The question addressed in this paper is to find a bidimensional representation of textual documents for the problem of text categorisation. The projection of documents is performed following subsequent steps. The main idea is to consider a possible double aspect of the importance of a word: the local importance in a category, and the global importance in the rest of the categories. This information is combined properly and summarized in two coordinates. Then, a machine learning method may be used in this simple bidimensional space to classify the documents. The results that can be obtained in this space are satisfactory with respect to the best state-of-the-art performances.

A Bidimensional View of Documents for Text Categorisation

DI NUNZIO, GIORGIO MARIA
2004

Abstract

The question addressed in this paper is to find a bidimensional representation of textual documents for the problem of text categorisation. The projection of documents is performed following subsequent steps. The main idea is to consider a possible double aspect of the importance of a word: the local importance in a category, and the global importance in the rest of the categories. This information is combined properly and summarized in two coordinates. Then, a machine learning method may be used in this simple bidimensional space to classify the documents. The results that can be obtained in this space are satisfactory with respect to the best state-of-the-art performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/1468206
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