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Recentred local profiles for authorship attribution

journal contribution
posted on 2012-07-01, 00:00 authored by R Layton, P Watters, Richard DazeleyRichard Dazeley
Authorship attribution methods aim to determine the author of a document, by using information gathered from a set of documents with known authors. One method of performing this task is to create profiles containing distinctive features known to be used by each author. In this paper, a new method of creating an author or document profile is presented that detects features considered distinctive, compared to normal language usage. This recentreing approach creates more accurate profiles than previous methods, as demonstrated empirically using a known corpus of authorship problems. This method, named recentred local profiles, determines authorship accurately using a simple 'best matching author' approach to classification, compared to other methods in the literature. The proposed method is shown to be more stable than related methods as parameter values change. Using a weighted voting scheme, recentred local profiles is shown to outperform other methods in authorship attribution, with an overall accuracy of 69.9% on the ad-hoc authorship attribution competition corpus, representing a significant improvement over related methods.

History

Journal

Natural language engineering

Volume

18

Pagination

293-312

Location

Cambridge, Eng.

ISSN

1351-3249

eISSN

1469-8110

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2011, Cambridge University Press

Issue

3

Publisher

Cambridge University Press

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