Patterns of local discourse coherence as a feature for authorship attribution.
We define a model of discourse coherence based on Barzilay and Lapata’s entity grids as a stylometric feature for authorship attribution. Unlike standard lexical and character-level features, it operates at a discourse (cross-sentence) level. We test it against and in combination with standard featu...
| Published in: | Literary & Linguistic Computing Vol. 29; no. 2; pp. 191 - 199 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Jun2014
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=96092900&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 96092900 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Jun2014 vid: 29 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 96092900 10.1093/llc/fqt021 ppf: 191 ppct: 8 formats: fmt: @attributes: type: P size: 96KB tig: atl: Patterns of local discourse coherence as a feature for authorship attribution. aug: au: Feng, Vanessa Wei Hirst, Graeme affil: University of Toronto, Canada su: Authorship Stylometry Cybernetics Word frequency Books sug: subj: Authorship Stylometry Cybernetics Word frequency Books ab: We define a model of discourse coherence based on Barzilay and Lapata’s entity grids as a stylometric feature for authorship attribution. Unlike standard lexical and character-level features, it operates at a discourse (cross-sentence) level. We test it against and in combination with standard features on nineteen book-length texts by nine nineteenth-century authors. We find that coherence alone performs often as well as and sometimes better than standard features, though a combination of the two has the highest performance overall. We observe that despite the difference in levels, there is a correlation in performance of the two kinds of features. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Literary & Linguistic Computing holder: Oxford University Press / USA dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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