Standard

Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles. / Selivanova, I. V.; Kosyakov, D. V.; Dubovitskii, D. A. et al.

In: Automatic documentation and mathematical linguistics, Vol. 55, No. 4, 07.2021, p. 178-189.

Research output: Contribution to journal › Article › peer-review

Harvard

Selivanova, IV, Kosyakov, DV, Dubovitskii, DA & Guskov, AE 2021, 'Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles', Automatic documentation and mathematical linguistics, vol. 55, no. 4, pp. 178-189. https://doi.org/10.3103/S0005105521040075

APA

Selivanova, I. V., Kosyakov, D. V., Dubovitskii, D. A., & Guskov, A. E. (2021). Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles. Automatic documentation and mathematical linguistics, 55(4), 178-189. https://doi.org/10.3103/S0005105521040075

Vancouver

Selivanova IV, Kosyakov DV, Dubovitskii DA, Guskov AE. Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles. Automatic documentation and mathematical linguistics. 2021 Jul;55(4):178-189. doi: 10.3103/S0005105521040075

Author

Selivanova, I. V. ; Kosyakov, D. V. ; Dubovitskii, D. A. et al. / Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles. In: Automatic documentation and mathematical linguistics. 2021 ; Vol. 55, No. 4. pp. 178-189.

BibTeX

@article{a709157247814ab08c708318b7eb9e7f,
title = "Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles",
abstract = "In this article we consider a fundamentally new information-theoretic approach to the classification of scientific texts based on compression algorithms. An analysis using the example of the comparative classification of full-text documents from arXiv.org and short annotations from Scopus showed that the accuracy of the proposed method is 87-92% and, in general, is not inferior to the existing ones. These conclusions were confirmed by an expert assessment.",
keywords = "text classification methods, data compression algorithms, scientific texts, arXiv.org, Scopus, k-nearest neighbors, logistic regression, random forests, naive Bayesian classification, support vector machines, K-NEAREST NEIGHBOR",
author = "Selivanova, {I. V.} and Kosyakov, {D. V.} and Dubovitskii, {D. A.} and Guskov, {A. E.}",
year = "2021",
month = jul,
doi = "10.3103/S0005105521040075",
language = "English",
volume = "55",
pages = "178--189",
journal = "Automatic documentation and mathematical linguistics",
issn = "0005-1055",
publisher = "Allerton Press Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles

AU - Selivanova, I. V.

AU - Kosyakov, D. V.

AU - Dubovitskii, D. A.

AU - Guskov, A. E.

PY - 2021/7

Y1 - 2021/7

N2 - In this article we consider a fundamentally new information-theoretic approach to the classification of scientific texts based on compression algorithms. An analysis using the example of the comparative classification of full-text documents from arXiv.org and short annotations from Scopus showed that the accuracy of the proposed method is 87-92% and, in general, is not inferior to the existing ones. These conclusions were confirmed by an expert assessment.

AB - In this article we consider a fundamentally new information-theoretic approach to the classification of scientific texts based on compression algorithms. An analysis using the example of the comparative classification of full-text documents from arXiv.org and short annotations from Scopus showed that the accuracy of the proposed method is 87-92% and, in general, is not inferior to the existing ones. These conclusions were confirmed by an expert assessment.

KW - text classification methods

KW - data compression algorithms

KW - scientific texts

KW - arXiv.org

KW - Scopus

KW - k-nearest neighbors

KW - logistic regression

KW - random forests

KW - naive Bayesian classification

KW - support vector machines

KW - K-NEAREST NEIGHBOR

U2 - 10.3103/S0005105521040075

DO - 10.3103/S0005105521040075

M3 - Article

VL - 55

SP - 178

EP - 189

JO - Automatic documentation and mathematical linguistics

JF - Automatic documentation and mathematical linguistics

SN - 0005-1055

IS - 4

ER -

ID: 34690303