SOTAVerified

A Data-Oriented Model of Literary Language

2017-01-12EACL 2017Code Available0· sign in to hype

Andreas van Cranenburgh, Rens Bod

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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.

Tasks

Reproductions