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A Thematic Coherence Study of a Bilingual Corpus of Articles on Oil and Gas Research

A Thematic Coherence Study of a Bilingual Corpus of Articles on Oil and Gas Research ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2019, Vol. 53, No. 3, pp. 138–142. © Allerton Press, Inc., 2019. Russian Text © The Author(s), 2019, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2019, No. 6, pp. 22–27. AUTOMATION OF TEXT PROCESSING A Thematic Coherence Study of a Bilingual Corpus of Articles on Oil and Gas Research a, b, c, b, d, F. V. Krasnov *, M. E. Shvartsman **, A. V. Dimentov ***, and A. I. Sen **** Gazpromneft Research and Development Center, St. Petersburg, 190000 Russia National Electronic Information Consortium, Moscow, 115114 Russia Russian State Library, Moscow, 119019 Russia St. Petersburg State University, St. Petersburg, 199034 Russia *e-mail: Krasnov.FV@gazpromneft-ntc.ru **e-mail: shvar@neicon.ru ***e-mail: adimentov@neicon.ru ****e-mail: anastasiia.sen@apmath.spbgu.ru Received April 8, 2019 Abstract—Structural differences between scientif ic articles that arise from their translation from Russian into English are studied using the modal topic modeling technique. Each collected document is represented by two modes, that is, English and Russian. As a result of the topic modeling, the Φ and Θ bimodal matrices are obtained. Analysis of the Φ matrix showed that the topics were divided according to the degree of conformity between Russian and English terms when the words http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Documentation and Mathematical Linguistics Springer Journals

A Thematic Coherence Study of a Bilingual Corpus of Articles on Oil and Gas Research

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Allerton Press, Inc.
Subject
Computer Science; Information Storage and Retrieval
ISSN
0005-1055
eISSN
1934-8371
DOI
10.3103/S0005105519030075
Publisher site
See Article on Publisher Site

Abstract

ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2019, Vol. 53, No. 3, pp. 138–142. © Allerton Press, Inc., 2019. Russian Text © The Author(s), 2019, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2019, No. 6, pp. 22–27. AUTOMATION OF TEXT PROCESSING A Thematic Coherence Study of a Bilingual Corpus of Articles on Oil and Gas Research a, b, c, b, d, F. V. Krasnov *, M. E. Shvartsman **, A. V. Dimentov ***, and A. I. Sen **** Gazpromneft Research and Development Center, St. Petersburg, 190000 Russia National Electronic Information Consortium, Moscow, 115114 Russia Russian State Library, Moscow, 119019 Russia St. Petersburg State University, St. Petersburg, 199034 Russia *e-mail: Krasnov.FV@gazpromneft-ntc.ru **e-mail: shvar@neicon.ru ***e-mail: adimentov@neicon.ru ****e-mail: anastasiia.sen@apmath.spbgu.ru Received April 8, 2019 Abstract—Structural differences between scientif ic articles that arise from their translation from Russian into English are studied using the modal topic modeling technique. Each collected document is represented by two modes, that is, English and Russian. As a result of the topic modeling, the Φ and Θ bimodal matrices are obtained. Analysis of the Φ matrix showed that the topics were divided according to the degree of conformity between Russian and English terms when the words

Journal

Automatic Documentation and Mathematical LinguisticsSpringer Journals

Published: Aug 26, 2019

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