Identifying literary texts with bigrams

Andreas van Cranenburgh; Corina Koolen

doi:10.3115/v1/W15-0707

Identifying literary texts with bigrams

Andreas van Cranenburgh, Corina Koolen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

11 Citations (Scopus)

113 Downloads (Pure)

Abstract

We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with machine learning models show that it is possible to automatically distinguish novels that are seen as highly literary from those that are seen as less literary, using surprisingly simple textual features. The most discriminating features of our classification model indicate that genre might be a confounding factor, but a regression model shows that we can also explain variation between highly literary novels from less literary ones within genre.

Original language	English
Title of host publication	Proceedings of CLFL
Place of Publication	Denver
Publisher	Association for Computational Linguistics (ACL)
Pages	58-67
Number of pages	10
DOIs	https://doi.org/10.3115/v1/W15-0707
Publication status	Published - Jun-2015

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10.3115/v1/W15-0707Licence: CC BY-NC-SA

Identifying Literary Texts with BigramsFinal publisher's version, 240 KBLicence: CC BY-NC-SA

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title = "Identifying literary texts with bigrams",

abstract = "We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with machine learning models show that it is possible to automatically distinguish novels that are seen as highly literary from those that are seen as less literary, using surprisingly simple textual features. The most discriminating features of our classification model indicate that genre might be a confounding factor, but a regression model shows that we can also explain variation between highly literary novels from less literary ones within genre.",

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AB - We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with machine learning models show that it is possible to automatically distinguish novels that are seen as highly literary from those that are seen as less literary, using surprisingly simple textual features. The most discriminating features of our classification model indicate that genre might be a confounding factor, but a regression model shows that we can also explain variation between highly literary novels from less literary ones within genre.

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M3 - Conference contribution

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BT - Proceedings of CLFL

PB - Association for Computational Linguistics (ACL)

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Identifying literary texts with bigrams

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