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Literary and Linguistic Computing Advance Access originally published online on October 1, 2007
Literary and Linguistic Computing 2007 22(4):405-417; doi:10.1093/llc/fqm023
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© The Author 2007. Published by Oxford University Press on behalf of ALLC and ACH. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts

Graeme Hirst and Ol’ga Feiguina 1

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Correspondence: Graeme Hirst, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, M5S 3G4. E-mail: gh{at}cs.toronto.edu

  Abstract

We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Brontë, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long.


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