Email this article Classification by compression: Application of information-theory methods for the identification of themes of scientific texts
- Authors: Selivanova I.V.1, Ryabko B.Y.2,3, Guskov A.E.2,3
-
Affiliations:
- The State Public Scientific Technological Library, Siberian Branch
- Novosibirsk State University
- Institute of Computational Technologies, Siberian Branch
- Issue: Vol 51, No 3 (2017)
- Pages: 120-126
- Section: Information Analysis
- URL: https://journals.rcsi.science/0005-1055/article/view/150170
- DOI: https://doi.org/10.3103/S0005105517030116
- ID: 150170
Cite item
Open Access
Access granted
Subscription Access
Abstract
A method for automatic classification of scientific texts based on data compression is proposed. The method is implemented and investigated based on the data from an archive of scientific texts (arXiv.org) and in the CyberLeninka scientific electronic library (CyberLeninka.ru). Experiments showed that the method correctly identified the themes of scientific texts with a probability of 75–95%; its accuracy depends on the quality of the original data.
About the authors
I. V. Selivanova
The State Public Scientific Technological Library, Siberian Branch
Author for correspondence.
Email: selivanova@ict.sbras.ru
Russian Federation, Novosibirsk, 123298
B. Ya. Ryabko
Novosibirsk State University; Institute of Computational Technologies, Siberian Branch
Email: selivanova@ict.sbras.ru
Russian Federation, Novosibirsk, 630090; Novosibirsk, 630090
A. E. Guskov
Novosibirsk State University; Institute of Computational Technologies, Siberian Branch
Email: selivanova@ict.sbras.ru
Russian Federation, Novosibirsk, 630090; Novosibirsk, 630090
Supplementary files
