License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2019.8
URN: urn:nbn:de:0030-drops-103723
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Kobbe, Jonathan ; Opitz, Juri ; Becker, Maria ; Hulpus, Ioana ; Stuckenschmidt, Heiner ; Frank, Anette

Exploiting Background Knowledge for Argumentative Relation Classification

OASIcs-LDK-2019-8.pdf (0.5 MB)


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.

BibTeX - Entry

  author =	{Jonathan Kobbe and Juri Opitz and Maria Becker and Ioana Hulpus and Heiner Stuckenschmidt and Anette Frank},
  title =	{{Exploiting Background Knowledge for Argumentative Relation Classification}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{8:1--8:14},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Maria Eskevich and Gerard de Melo and Christian F{\"a}th and John P. McCrae and Paul Buitelaar and Christian Chiarcos and Bettina Klimek and Milan Dojchinovski},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-103723},
  doi =		{10.4230/OASIcs.LDK.2019.8},
  annote =	{Keywords: argument structure analysis, background knowledge, argumentative functions, argument classification, commonsense knowledge relations}

Keywords: argument structure analysis, background knowledge, argumentative functions, argument classification, commonsense knowledge relations
Collection: 2nd Conference on Language, Data and Knowledge (LDK 2019)
Issue Date: 2019
Date of publication: 16.05.2019

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