A Data-Oriented Model of Literary Language

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1228–1238, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics A Data-Oriented Model of Literary Language Andreas van Cranenburgh Institut f¨ur Sprache und Information Heinrich Heine University D¨usseldorf cranenburgh@phil.hhu.de Rens Bod Institute for Logic, Language and Computation, University of Amsterdam rens.bod@uva.nl Abstract We consider the task of predicting how lit- erary a text is, with a gold standard from human ratings. Aside from a standard bi- gram 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. 1 Introduction What makes a literary novel literary? This seems first of all to be a value judgment; but to what ex- tent is this judgment arbitrary, determined by social factors, or predictable as a function of the text? The last explanation is associated with the concept of literariness, the hypothesized linguistic and formal properties that distinguish literary language from other language (Baldick, 2008). Although the defi- nition and demarcation of literature is fundamental to the field of literary studies, it has received sur- prisingly little empirical study. Common wisdom has it that literary distinction is attributed in social communication about novels and that it lies mostly outside of the text itself (Bourdieu, 1996), but an increasing number of studies argue that in addition to social and historical explanations, textual fea- tures of various complexity may also contribute to the perception of literature by readers (cf. Harris, 1995; McDonald, 2007). The current paper shows that not only lexical features but also hierarchical syntactic features and other textual characteristics contribute to explaining judgments of literature. Our main goal in this project is to answer the following question: are there particular textual con- ventions in literary novels that contribute to readers judging them to be literary? We address this ques- tion by building a model of literary evaluation to estimate the contribution of textual factors. This task has been considered before with a smaller set of novels (restricted to thrillers and literary nov- els), using bigrams (van Cranenburgh and Koolen, 2015). We extend this work by testing on a larger, more diverse corpus, and by applying rich syn- tactic features and several hand-picked features to the task. This task is first of all relevant to liter- ary studies—to reveal to what extent literature is empirically associated with textual characteristics. However, practical applications are also possible; e.g., an automated model could help a literary pub- lisher decide whether the work of a new author fits its audience; or it could be used as part of a recommender system for readers. Literary language is arguably a subjective no- tion. A gold standard could be based on the expert opinions of critics and literary prizes, but we …

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