Mining for characterising patterns in literature using correspondence analysis: an experiment on French novels.

This paper presents and describes a bottom-up methodology for the detection of stylistic traits in the syntax of literary texts. The extraction of syntactic patterns is performed blindly by a sequential pattern mining algorithm, while the identification of significant and interesting features is per...

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Publicado en:DHQ: Digital Humanities Quarterly Vol. 11; no. 2; pp. 131 - 145
Autores principales: Frontini, Francesca, Boukhaled, Mohamed Amine, Ganascia, Jean-Gabriel
Formato: Artículo
Publicado: Digital Humanities Quarterly 2017
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Mining for characterising patterns in literature using correspondence analysis: an experiment on French novels.
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        au:
          Frontini, Francesca
          Boukhaled, Mohamed Amine
          Ganascia, Jean-Gabriel
        affil:
          Université Paul-Valéry Montpellier 3 - Praxiling UMR 5267 CNRS - UPVM3
          Laboratoire d'Informatique de Paris 6 (LIP6 UPMC) / Labex OBVIL
      su:
        Literature
        French fiction
        Linguistics
        Normalization (Sociology)
        Social theory
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        subj:
          Literature
          French fiction
          Linguistics
          Normalization (Sociology)
          Social theory
      ab: This paper presents and describes a bottom-up methodology for the detection of stylistic traits in the syntax of literary texts. The extraction of syntactic patterns is performed blindly by a sequential pattern mining algorithm, while the identification of significant and interesting features is performed at a later stage by using correspondence analysis and by ranking patterns by contribution.
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      doctype: Article
      src: R
    language: English
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