Exploring erotics in Emily Dickinson's correspondence with text mining and visual interfaces
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Linguistic correlates of style: authorship classification with deep linguistic analysis features
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Searching with style: authorship attribution in classic literature
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
Unsupervised recognition of literal and non-literal use of idiomatic expressions
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Natural Language Processing with Python
Natural Language Processing with Python
Using Gaussian Mixture models to detect figurative language in context
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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This study aims to bridge the gap between subjective literary criticism and natural language processing by creating a model that emulates the results of a survey into literary tastes. A panel of human experts qualified segments of literary text according to how aesthetically pleasing they found them. These segments were then rated for literariness in an open survey using a Likert scale. Each segment was subjected to a parts-of-speech tagger using NLTK and the results compared with those of the survey. Using a Grounded Theory approach, experiments using various combinations of parts-of-speech were carried out in order to build a model that could replicate the results shown in the open survey. The success of this approach confirms the feasibility of using this method to create a more accurate and analytical model of literary criticism involving deeper stylistic markers.