Exploiting background knowledge for argumentative relation classification

Kobbe, Jonathan ; Opitz, Juri ; Becker, Maria ; Hulpus, Ioana ; Stuckenschmidt, Heiner ; Frank, Anette

DOI: https://doi.org/10.4230/OASIcs.LDK.2019.8
URL: http://drops.dagstuhl.de/opus/volltexte/2019/10372
Additional URL: https://nbn-resolving.org/urn:nbn:de:0030-drops-10...
URN: urn:nbn:de:0030-drops-105045
Document Type: Conference or workshop publication
Year of publication: 2019
Book title: 2nd Conference on Language, Data and Knowledge (LDK 2019)
The title of a journal, publication series: OASIcs - OpenAccess Series in Informatics
Volume: 70
Page range: 8:1-8:14
Conference title: LDK 2019
Location of the conference venue: Leipzig, Germany
Date of the conference: 20.-23.05.2019
Publisher: Eskevich, Maria
Place of publication: Wadern
Publishing house: Leibniz-Zentrum für Informatik
ISBN: 978-3-95977-105-4
ISSN: 2190-6807
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): argument structure analysis , background knowledge , argumentative functions , argument classification , commonsense knowledge relations
Abstract: Argumentative relation classification is the task of determining the type of relation (e.g., support or attack) that holds between two argument units. Current state-of-the-art models primarily exploit surface-linguistic features including discourse markers, modals or adverbials to classify argumentative relations. However, a system that performs argument analysis using mainly rhetorical features can be easily fooled by the stylistic presentation of the argument as opposed to its content, in cases where a weak argument is concealed by strong rhetorical means. This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based understanding and representation of arguments. We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. We evaluate our systems on three different datasets and show that the inclusion of background knowledge can improve the classification performance by considerable margins. Thus, our work offers a first step towards effective, knowledge-rich argument analysis.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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