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.
@article{arxiv.1701.03329,
title = {A Data-Oriented Model of Literary Language},
author = {Andreas van Cranenburgh and Rens Bod},
journal= {arXiv preprint arXiv:1701.03329},
year = {2017}
}