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...
| Publicado en: | DHQ: Digital Humanities Quarterly Vol. 11; no. 2; pp. 131 - 145 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Digital Humanities Quarterly
2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=125161977&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 125161977 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 19384122 77X0 jtl: DHQ: Digital Humanities Quarterly issn: 19384122 maglogo: N pubinfo: dt: 2017 vid: 11 iid: 2 pid: 47240 pub: Digital Humanities Quarterly artinfo: ui: 125161977 ppf: 131 ppct: 14 formats: tig: atl: Mining for characterising patterns in literature using correspondence analysis: an experiment on French novels. aug: 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 sug: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2017 holdings: @attributes: islocal: N |
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