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2017 | OriginalPaper | Buchkapitel

Natural Language Argumentation for Text Exploration

verfasst von : Elena Cabrio, Serena Villata

Erschienen in: Agents and Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

Argumentation mining aims at automatically extracting natural language arguments from textual documents. In the last years, it has become a hot topic due to its potential in processing information originating from the Web in innovative ways. In this paper, we propose to apply the argument mining pipeline to the text exploration task. First, starting from the arguments put forward in online debates, we introduce bipolar entailment graphs to predict the relation among the textual arguments, i.e., entailment or non entailment relation. Second, we exploit the well know formalism called abstract dialectical frameworks to define acceptance conditions answering the needs of the text exploration task. The evaluation of the proposed approach shows its feasibility.

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Vorheriges Kapitel Enhancing Support Vector Decoders by Integrating an Uncertainty Model

Nächstes Kapitel Instance Selection and Outlier Generation to Improve the Cascade Classifier Precision

As discussed also in the keynote talk of the Joint Symposium on Semantic Processing (http://jssp2013.fbk.eu/).

https://www.cs.cornell.edu/home/cardie/naacl-2nd-arg-mining/.

In the two-way classification task, contradiction and unknown relations are collapsed into a unique relation, i.e. non entailment.

[13] provides an overview of the recent advances in TE.

The Recognizing Textual Entailment (RTE) data are not suitable for our goal, since the pairs are not interconnected (i.e. they cannot be transformed into argumentation graphs).

http://idebate.org/.

http://bit.ly/RTE-challenge.

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The F-measure is a measure of accuracy. It considers both the precision and the recall of the test to compute the score.

Complexity results for ADFs have been studied by [6].

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Titel: Natural Language Argumentation for Text Exploration
verfasst von: Elena Cabrio
Serena Villata
Verlag: Springer International Publishing
Buch: Agents and Artificial Intelligence
Print ISBN: 978-3-319-53353-7

Electronic ISBN: 978-3-319-53354-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-53354-4_8

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