  We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary novels using a variety of lexical and syntactic features, and
explains 76.0 % of the variation in literary ratings.
