Article published in:Directions in Empirical Literary Studies: In honor of Willie van Peer
Edited by Sonia Zyngier, Marisa Bortolussi, Anna Chesnokova and Jan Auracher
[Linguistic Approaches to Literature 5] 2008
► pp. 175–191
Computationally Discriminating Literary from Non-Literary Texts
Three computational linguistic methods are presented to discriminate literary from non-literary texts. In the first study, a hierarchical clustering technique of results obtained from Latent Semantic Analysis showed a clustering of literary versus non-literary texts. The second study used the frequencies of shared bigrams across the text, resulting in a 100% correct classification of literary versus non-literary texts. The third study used unigrams yielding a 94% correct classification into literary versus non-literary texts. The final two studies using a larger sample of texts showed that the high classification performance cannot be attributed to specific texts. These findings provide evidence that distinguishing literature from non-literature can be done with high accuracy and with relatively simple computational linguistic techniques.
Keywords: bigram analysis, classification techniques, computational linguistics, genre, latent semantic analysis, stylistics
Published online: 15 May 2008
Cited by 5 other publications
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