Automatic Detection of Authorship Changes within Single
Documents

M.Sc. Thesis

Neil Graham

Department of Computer Science
University of Toronto

January 2000

One of the most difficult tasks facing anyone who must compile or maintain
any large, collaboratively-written document is to foster a consistent
style throughout.  In this thesis, we explore whether it is possible to
identify stylistic inconsistencies within documents even in principle,
given our understanding of how style can be captured statistically.

We carry out this investigation by computing stylistic statistics on very
small samples of text comprising a set of synthetic
collaboratively-written documents, and using these statistics to train and
test a series of neural networks.  We are able to show that this method
does allow us to recover the boundaries of authors' contributions. We find
that time-delay neural networks, hitherto ignored in this field, are
especially effective in this regard.  Along the way, we observe that
statistics characterizing the syntactic style of a passage appear to hold
much more information for small text samples than those concerned with
lexical choice or complexity.