Uso de Seleção de Características da Wikipedia na Classificação Automática de Textos.
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Data
2012-09-20
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Universidade Federal de Goiás
Resumo
The traditional methods of text classification typically represent documents only as a
set of words, also known as "Bag of Words"(BOW). Several studies have shown good
results on making use of thesauri and encyclopedias as external information sources,
aiming to expand the BOW representation by the identification of synonymy and
hyponymy relationships between present terms in a document collection. However,
the expansion process may introduce terms that lead to an erroneous classification. In
this paper, we propose the use of feature selection measures in order to select features
extracted from Wikipedia in order to improve the efectiveness of the expansion
process. The study also proposes a feature selection measure called Tendency Factor
to One Category (TF1C), so that the experiments showed that this measure proves
to be competitive with the other measures Information Gain, Gain Ratio and Chisquared,
in the process, delivering the best gains in microF1 and macroF1, in most
experiments. The full use of features selected in this process showed to be more stable
in assisting the classification, while it showed lower performance on restricting its
insertion only to documents of the classes in which these features are well punctuated
by the selection measures. When applied in the Reuters-21578, Ohsumed first -
20000 and 20Newsgroups collections, our approach to feature selection allowed the
reduction of noise insertion inherent in the expansion process, and improved the
results of use hyponyms, and demonstrated that the synonym relationship from
Wikipedia can also be used in the document expansion, increasing the efectiveness
of the automatic text classification.
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Citação
ALVARENGA, Leonel Diógenes Carvalhaes. Uso de Seleção de Características da Wikipedia na Classificação Automática de Textos. 2012. 114 f. - Dissertação (Mestrado em) - Universidade Federal de Goiás, Goiânia, 2012