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End-to-End Argumentation Knowledge Graph Construction

Authors: Khalid Al-Khatib; Yufang Hou; Henning Wachsmuth; Charles Jochim; Francesca Bonin; Benno Stein;

End-to-End Argumentation Knowledge Graph Construction

Abstract

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

Country
Netherlands
Subjects by Vocabulary

Microsoft Academic Graph classification: Argumentative Information retrieval Computer science Exploratory search Argumentation theory Knowledge graph End-to-end principle Question answering Graph (abstract data type)

Keywords

General Medicine, Computational Argumentation, Argumentation Knowledge Graph

48 references, page 1 of 5

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Bex, F.; Lawrence, J.; Snaith, M.; and Reed, C. 2013. Implementing the argument web. Communications of the ACM 56(10):66-73.

Botschen, T.; Sorokin, D.; and Gurevych, I. 2018. Frame- and entity-based knowledge for common-sense argumentative reasoning. In 5th Workshop on Argument Mining, 90-96.

Choi, Y., and Wiebe, J. 2014. +/-effectwordnet: Sense-level lexicon acquisition for opinion inference. In EMNLP'14, 1181-1191.

Dasgupta, T.; Saha, R.; Dey, L.; and Naskar, A. 2018. Automatic extraction of causal relations from text using linguistically informed deep neural networks. In SIGdial'18, 306-316.

Dozat, T.; Qi, P.; and D. Manning, C. 2017. Stanford's graphbased neural dependency parser at the conll 2017 shared task. In CoNLL'17 Shared Task, 20-30.

Dunietz, J.; Levin, L.; and Carbonell, J. 2017. Automatically tagging constructions of causation and their slot-fillers. TACL 5:117- 133. [OpenAIRE]

Feng, V. W., and Hirst, G. 2011. Classifying arguments by scheme.

In ACL'11, 987-996.

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    18
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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18
Top 10%
Top 10%
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263
192
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