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.
", download = "http://ftp.cs.toronto.edu/pub/gh/Graham-thesis.pdf" }