A Data-Oriented Model of Literary Language

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Authors Andreas van Cranenburgh, Rens Bod
Journal/Conference Name EACL 2017 4
Paper Category
Paper 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.
Date of publication 2017
Code Programming Language Python
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