Computational Linguistics and Intellectual Technologies
Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts
Publication type: Proceedings Article
Publication date: 2021-06-19
Quartile SCImago
— Quartile WOS
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Impact factor: —
ISSN: 20757182
Abstract
Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure. A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora. This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssays). To make it possible to use these two corpora combined, we propose a Joint Argument Annotation Scheme based on the schemes used in ArgMicro and PersEssays. We solve the problem of classifying argumentative discourse units (ADUs) into two classes – “pro” (“for”) and “opp” (“against”) using traditional machine learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT model). An ensemble of XGBoost and BERT models was proposed, which showed the highest performance of ADUs classification for both corpora.
Citations by journals
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1
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Pattern Recognition and Image Analysis
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Pattern Recognition and Image Analysis
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Program systems theory and applications
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Program systems theory and applications
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Citations by publishers
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1
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Pleiades Publishing
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Pleiades Publishing
1 publication, 50%
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Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS)
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Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS)
1 publication, 50%
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1
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Fishcheva I. N., Goloviznina V. S., Kotelnikov E. V. Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts // Computational Linguistics and Intellectual Technologies. 2021.
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Fishcheva I. N., Goloviznina V. S., Kotelnikov E. V. Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts // Computational Linguistics and Intellectual Technologies. 2021.
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TY - CPAPER
DO - 10.28995/2075-7182-2021-20-246-258
UR - https://doi.org/10.28995%2F2075-7182-2021-20-246-258
TI - Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts
T2 - Computational Linguistics and Intellectual Technologies
AU - Fishcheva, I N
AU - Goloviznina, V S
AU - Kotelnikov, E V
PY - 2021
DA - 2021/06/19 00:00:00
PB - Russian State University for the Humanities
SN - 2075-7182
ER -
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@inproceedings{2021_Fishcheva,
author = {I N Fishcheva and V S Goloviznina and E V Kotelnikov},
title = {Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts},
year = {2021},
month = {jun},
publisher = {Russian State University for the Humanities}
}
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