Research output: Contribution to journal › Conference article › peer-review
Identification of connected arguments based on reasoning schemes “from expert opinion”. / Salomatina, N. V.; Kononenko, I. S.; Sidorova, E. A. et al.
In: Journal of Physics: Conference Series, Vol. 1715, No. 1, 012013, 04.01.2021.Research output: Contribution to journal › Conference article › peer-review
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TY - JOUR
T1 - Identification of connected arguments based on reasoning schemes “from expert opinion”
AU - Salomatina, N. V.
AU - Kononenko, I. S.
AU - Sidorova, E. A.
AU - Pimenov, I. S.
N1 - Funding Information: The research has been supported by Russian Foundation for Basic Research (Grants 18-00-01376 (18-00-00889) and 18-00-01376 (18-00-00760)). Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/4
Y1 - 2021/1/4
N2 - The work presented describes a combined approach to the partial extraction of the argumentative structure of a text that can be employed in the absence of sufficient annotated data to apply efficiently the machine learning methods for the direct detection of arguments and their relations. In this case, argument identification is performed by using the patterns of argumentation indicators created by a linguist and automatically expanded. These patterns enable the recognition of specific argument types with fine precision. In this study, arguments “from expert opinion” serve as such a pivot type. Besides, potential relations between recognized arguments are analyzed by dividing the text into superphrasal units (fragments united by one topic). The criterion for connecting arguments in an argumentative structure is their inclusion in the same superphrasal unit. An experiment for identifying potentially related arguments is conducted on a set of popular science texts with a minimum size of 1000 words.
AB - The work presented describes a combined approach to the partial extraction of the argumentative structure of a text that can be employed in the absence of sufficient annotated data to apply efficiently the machine learning methods for the direct detection of arguments and their relations. In this case, argument identification is performed by using the patterns of argumentation indicators created by a linguist and automatically expanded. These patterns enable the recognition of specific argument types with fine precision. In this study, arguments “from expert opinion” serve as such a pivot type. Besides, potential relations between recognized arguments are analyzed by dividing the text into superphrasal units (fragments united by one topic). The criterion for connecting arguments in an argumentative structure is their inclusion in the same superphrasal unit. An experiment for identifying potentially related arguments is conducted on a set of popular science texts with a minimum size of 1000 words.
UR - http://www.scopus.com/inward/record.url?scp=85100821216&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1715/1/012013
DO - 10.1088/1742-6596/1715/1/012013
M3 - Conference article
AN - SCOPUS:85100821216
VL - 1715
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012013
T2 - International Conference on Marchuk Scientific Readings 2020, MSR 2020
Y2 - 19 October 2020 through 23 October 2020
ER -
ID: 27879627