Segmenting documents by stylistic character

Authors:
Neil Graham;Graeme Hirst;Bhaskara Marthi
Affiliations:
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 e-mail: gh@cs.toronto.edu;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 e-mail: gh@cs.toronto.edu;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 e-mail: gh@cs.toronto.edu
Venue:
Natural Language Engineering
Year:
2005

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Abstract

As part of a larger project to develop an aid for writers that would help to eliminate stylistic inconsistencies within a document, we experimented with neural networks to find the points in a text at which its stylistic character changes. Our best results, well above baseline, were achieved with time-delay networks that used features related to the author's syntactic preferences, whereas low-level and vocabulary-based features were not found to be useful. An alternative approach with character bigrams was not successful.