Open Access
Open access
Lecture Notes in Computer Science, pages 134-144

Cross-Lingual Argumentation Mining for Russian Texts

Publication typeBook Chapter
Publication date2019-12-14
Quartile SCImago
Q3
Quartile WOS
Impact factor
ISSN03029743, 16113349, 18612075, 18612083
Abstract
Argumentation mining refers to automatic extraction of arguments and their relations from texts. This field has been evolving rapidly in recent years, but there is almost no research for the Russian language. The present study is an attempt to overcome this gap. Firstly, we create the first argument-annotated corpus of Russian based on Argumentative Microtext Corpus and make it publicly available. Secondly, we study the importance of various feature types. Contextual and lexical features turn out to be the most significant. Thirdly, we evaluate the performance of various classifiers for argumentation mining. Bagging and XGBoost classifiers give the best results. Fourthly, we assess the possibility of using several machine translation systems (Google Translate, Yandex.Translate and Promt) for automatic creating of argument-annotated corpora. Google Translate appears to be the best system to reach this goal.

Citations by journals

1
Applied Sciences (Switzerland)
Applied Sciences (Switzerland), 1, 20%
Applied Sciences (Switzerland)
1 publication, 20%
NSU Vestnik. Series: Linguistics and Intercultural Communication
NSU Vestnik. Series: Linguistics and Intercultural Communication, 1, 20%
NSU Vestnik. Series: Linguistics and Intercultural Communication
1 publication, 20%
Pattern Recognition and Image Analysis
Pattern Recognition and Image Analysis, 1, 20%
Pattern Recognition and Image Analysis
1 publication, 20%
Program systems theory and applications
Program systems theory and applications, 1, 20%
Program systems theory and applications
1 publication, 20%
1

Citations by publishers

1
Multidisciplinary Digital Publishing Institute (MDPI)
Multidisciplinary Digital Publishing Institute (MDPI), 1, 20%
Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 20%
Novosibirsk State University (NSU)
Novosibirsk State University (NSU), 1, 20%
Novosibirsk State University (NSU)
1 publication, 20%
Pleiades Publishing
Pleiades Publishing, 1, 20%
Pleiades Publishing
1 publication, 20%
Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS)
Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS), 1, 20%
Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS)
1 publication, 20%
1
  • We do not take into account publications that without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.
Metrics
Share
Cite this
GOST |
Cite this
GOST Copy
Fishcheva I., Kotelnikov E. Cross-Lingual Argumentation Mining for Russian Texts // Lecture Notes in Computer Science. 2019. pp. 134-144.
GOST all authors (up to 50) Copy
Fishcheva I., Kotelnikov E. Cross-Lingual Argumentation Mining for Russian Texts // Lecture Notes in Computer Science. 2019. pp. 134-144.
RIS |
Cite this
RIS Copy
TY - GENERIC
DO - 10.1007/978-3-030-37334-4_12
UR - https://doi.org/10.1007%2F978-3-030-37334-4_12
TI - Cross-Lingual Argumentation Mining for Russian Texts
T2 - Lecture Notes in Computer Science
AU - Fishcheva, Irina
AU - Kotelnikov, Evgeny
PY - 2019
DA - 2019/12/14 00:00:00
PB - Springer Nature
SP - 134-144
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
BibTex
Cite this
BibTex Copy
@incollection{2019_Fishcheva
author = {Irina Fishcheva and Evgeny Kotelnikov},
title = {Cross-Lingual Argumentation Mining for Russian Texts},
publisher = {Springer Nature},
year = {2019},
pages = {134--144},
month = {dec}
}
Found error?