Using complex networks for text classification: Discriminating informative and imaginative documents

Autor(es)

de Arruda, Henrique F. ^[1] ; Costa, Luciano da F. ^[2] ; Amancio, Diego R. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo Sao Carlos, Inst Math & Comp Sci, Sao Paulo - Brazil

^[2] Univ Sao Paulo Sao Carlos, Sao Carlos Inst Phys, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EPL; v. 113, n. 2 JAN 2016.
• Citações Web of Science: 6

Resumo

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts. Copyright (C) EPLA, 2016 (AU)

• Processo FAPESP: 14/20830-0 - Modelagem e reconhecimento de padrões em textos com redes complexas
• Beneficiário: Diego Raphael Amancio
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/50986-7 - Graph spectra and complex network evolution
• Beneficiário: Luciano da Fontoura Costa
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Linha de fomento: Auxílio à Pesquisa - Temático