TY - GEN

T1 - The Combined Approach to Identifying Argumentation Structures in Short Scientific Papers

AU - Zasypkin, Alexander S.

AU - Pimenov, Ivan S.

AU - Salomatina, Natalia V.

N1 - The research was conducted within the framework of the state contract of the Sobolev Institute of Mathematics (projects no. FWNF-2022-0015). Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - The paper described the method for identifying argumentation structures in scientific texts in Russian language. This approach is aimed at automating argumentation annotation of text sets. It combines the machine learning methods and rule-based search through patterns that are based on a dictionary of argumentation markers. The combined method covers the four stages of modelling the argumentation structure of a text by an annotator: 1) identification of statements in a text, their classification into argumentative and non-argumentative; 2) detection of statements connections; 3) specification of statement roles in arguments (premises, conclusions); 4) identification of exact reasoning model in an argument (Analogy, Example, Verbal Classification). Stages 1) and 3) employ the machine learning methods (LogReg, SVM, MLP, MNB), the stage 2) uses the markers dictionary, the stage 4) relies on search patterns in form of regular expressions. The dataset for evaluating the method consists of argumentation annotations for 29 texts of short scientific papers from two thematic areas (information science and linguistics). These annotations are constructed by human experts with the ArgNetBankStudio platform and contain 1259 arguments and 1309 statements. The paper provided the quality scores for the identification of argumentation components at every stage. These scores showed that the combined method is applicable to the partial automatization of annotating argumentation.

AB - The paper described the method for identifying argumentation structures in scientific texts in Russian language. This approach is aimed at automating argumentation annotation of text sets. It combines the machine learning methods and rule-based search through patterns that are based on a dictionary of argumentation markers. The combined method covers the four stages of modelling the argumentation structure of a text by an annotator: 1) identification of statements in a text, their classification into argumentative and non-argumentative; 2) detection of statements connections; 3) specification of statement roles in arguments (premises, conclusions); 4) identification of exact reasoning model in an argument (Analogy, Example, Verbal Classification). Stages 1) and 3) employ the machine learning methods (LogReg, SVM, MLP, MNB), the stage 2) uses the markers dictionary, the stage 4) relies on search patterns in form of regular expressions. The dataset for evaluating the method consists of argumentation annotations for 29 texts of short scientific papers from two thematic areas (information science and linguistics). These annotations are constructed by human experts with the ArgNetBankStudio platform and contain 1259 arguments and 1309 statements. The paper provided the quality scores for the identification of argumentation components at every stage. These scores showed that the combined method is applicable to the partial automatization of annotating argumentation.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85171975805&origin=inward&txGid=c20cc742e585192bfc7f58400ef144b7

UR - https://www.mendeley.com/catalogue/344983fd-2c23-3b90-996e-f8ae133ff208/

U2 - 10.1109/edm58354.2023.10225223

DO - 10.1109/edm58354.2023.10225223

M3 - Conference contribution

SN - 9798350336870

SP - 1800

EP - 1805

BT - 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -