Information theory unravels the subtext in Chekhov

The present paper reports a pilot study to approach the subtext in Chekhov’s short-story "Ward No. 6" by means of information theory. The original text is enriched by glosses by which we intend to make explicit the implicit knowledge conveyed by the original text, i.e., the subtext. We generated several text variants with meaningful enrichments and one fake variant that served as a baseline. We could not observe that semantic surprisal as a feature of words and uniform information density have a subtext effect throughout all text variants. However, it turned out that kurtosis and skewness are suitable classification criteria to distinguish meaningful enrichments from fake enrichments.

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Publication Details

Journal
Digital humanities quarterly
Published
2025-07-18
DOI
https://doi.org/10.63744/sbxyn99tr6mf
Primary Topic
Authorship Attribution and Profiling
Type
article
Field-Weighted Citation Impact
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article

Information theory unravels the subtext in Chekhov

Matthias Irmer, Olav Mueller-Reichau, Michael Richter, J. Nathanael Philipp
Digital humanities quarterly
Authorship Attribution and Profiling
article

Information theory unravels the subtext in Chekhov

Matthias Irmer, Olav Mueller-Reichau, Michael Richter, J. Nathanael Philipp
article en

Abstract

The present paper reports a pilot study to approach the subtext in Chekhov’s short-story "Ward No. 6" by means of information theory. The original text is enriched by glosses by which we intend to make explicit the implicit knowledge conveyed by the original text, i.e., the subtext. We generated several text variants with meaningful enrichments and one fake variant that served as a baseline. We could not observe that semantic surprisal as a feature of words and uniform information density have a subtext effect throughout all text variants. However, it turned out that kurtosis and skewness are suitable classification criteria to distinguish meaningful enrichments from fake enrichments.

Digital humanities quarterlyVol. 19(2)
Openalex Percentile: Top 12%
Authorship Attribution and Profiling
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