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MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain

MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain

Authors :
Schrader, Timo Pierre
Bürkle, Teresa
Henning, Sophie
Tan, Sherry
Finco, Matteo
Grünewald, Stefan
Indrikova, Maira
Hildebrand, Felix
Friedrich, Annemarie
Publication Year :
2023

Abstract

Scientific publications follow conventionalized rhetorical structures. Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence states a Motivation, a Result or Background information, has been proposed to improve processing of scholarly documents. In this work, we adapt and extend this idea to the domain of materials science research. We present and release a new dataset of 50 manually annotated research articles. The dataset spans seven sub-topics and is annotated with a materials-science focused multi-label annotation scheme for AZ. We detail corpus statistics and demonstrate high inter-annotator agreement. Our computational experiments show that using domain-specific pre-trained transformer-based text encoders is key to high classification performance. We also find that AZ categories from existing datasets in other domains are transferable to varying degrees.<br />Comment: 15 pages, 2 figures, 14 tables, to be published in "Proceedings of the 4th Workshop on Computational Approaches to Discourse"

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2307.02340
Document Type :
Working Paper