The Study of Argumentative Relations in Popular Science Discourse

Kononenko, Irina; Sidorova, Elena A.; Akhmadeeva, Irina R.

doi:10.1007/978-3-030-59535-7_23

Cited by 1 publication

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“…Kononenko et al [12] studied argumentation using the comparative analysis of discourse structures. Various types of argument structures were considered.…”

Section: Argumentation Mining In Russianmentioning

confidence: 99%

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Fishcheva¹,

Goloviznina²,

Kotelnikov³

2021

Computational Linguistics and Intellectual Technologies

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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.

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“…Kononenko et al [12] studied argumentation using the comparative analysis of discourse structures. Various types of argument structures were considered.…”

Section: Argumentation Mining In Russianmentioning

confidence: 99%