Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of EACL |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 1228-1238 |
| Number of pages | 11 |
| Publication status | Published - 2017 |