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• W2583135790 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." @default.
• W2583135790 created "2017-02-10" @default.
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• W2583135790 date "2017-01-01" @default.
• W2583135790 modified "2023-10-16" @default.
• W2583135790 title "A Data-Oriented Model of Literary Language" @default.
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• W2583135790 doi "https://doi.org/10.18653/v1/e17-1115" @default.
• W2583135790 hasPublicationYear "2017" @default.
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