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Using Data Compression to Build a Method for Statistically Verified Attribution of Literary Texts
Boris Ryabko1,2, Nadezhda Savina2
1Federal Research Center for Information and Computational Technologies of SB RAS, 630090 Novosibirsk, Russia.
Entropy (Basel, Switzerland)
|October 23, 2021
Summary
This study introduces a novel authorship attribution method for literary texts using data compression techniques. This approach provides statistically verifiable results for analyzing Russian literature authorship.
Area of Science:
- Computational Linguistics
- Literary Studies
- Digital Humanities
Background:
- Authorship attribution is crucial for literary analysis.
- Traditional methods may lack statistical rigor.
- Quantitative approaches offer new insights into literary studies.
Purpose of the Study:
- To propose a new methodology for literary text authorship attribution.
- To leverage data compression for statistically verified attribution.
- To apply the method to specific problems in Russian literature.
Main Methods:
- Utilizing data compressors as a core component of the attribution methodology.
- Developing a quantitative framework for analyzing textual features.
- Implementing statistical verification for attribution results.
Main Results:
- Demonstrated the efficacy of data compression for authorship attribution.
- Achieved statistically verified results in attribution tasks.
- Successfully applied the method to two distinct Russian literature attribution problems.
Conclusions:
- Data compression offers a robust and statistically sound method for authorship attribution.
- The proposed methodology advances quantitative literary studies.
- This approach has significant potential for literary scholarship, particularly in analyzing Russian literary works.
Keywords:
authorship attribution of literary textsdata compressionhypothesis testingquantitative study of literatureMore Related Videos
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