  Over the years there has been ongoing interest in detecting authorship of a
text based on statistical properties of the text, such as by using occurrence
rates of noncontextual words. In previous work, these techniques have been
used, for example, to determine authorship of all of \emph{The Federalist
Papers}. Such methods may be useful in more modern times to detect fake or AI
authorship. Progress in statistical natural language parsers introduces the
possibility of using grammatical structure to detect authorship. In this paper
we explore a new possibility for detecting authorship using grammatical
structural information extracted using a statistical natural language parser.
This paper provides a proof of concept, testing author classification based on
grammatical structure on a set of "proof texts," The Federalist Papers and
Sanditon which have been as test cases in previous authorship detection
studies. Several features extracted from the statistical natural language
parser were explored: all subtrees of some depth from any level; rooted
subtrees of some depth, part of speech, and part of speech by level in the
parse tree. It was found to be helpful to project the features into a lower
dimensional space. Statistical experiments on these documents demonstrate that
information from a statistical parser can, in fact, assist in distinguishing
authors.
