Mining bipolar argumentation frameworks from natural language text
File(s)final.pdf (611.77 KB)
Accepted version
Author(s)
Cocarascu, Oana
Toni, Francesca
Type
Conference Paper
Abstract
We describe a methodology for mining topic-dependent Bipolar
Argumentation Frameworks (BAFs) from natural language text.
Our focus is on identifying attack and support argumentative re-
lations between texts about the same topic, treating these texts as
arguments when they are argumentatively related to other texts.
We illustrate our methodology on a dataset of hotel reviews and
outline some possible applications using the BAFs resulting from
our methodology.
Argumentation Frameworks (BAFs) from natural language text.
Our focus is on identifying attack and support argumentative re-
lations between texts about the same topic, treating these texts as
arguments when they are argumentatively related to other texts.
We illustrate our methodology on a dataset of hotel reviews and
outline some possible applications using the BAFs resulting from
our methodology.
Date Issued
2017-06-16
Date Acceptance
2017-05-12
Citation
Proceedings of the 17th Workshop on Computational Models of Natural Argument co-located with ICAIL 2017,, 2017, pp.65-70
Start Page
65
End Page
70
Journal / Book Title
Proceedings of the 17th Workshop on Computational Models of Natural Argument co-located with ICAIL 2017,
Copyright Statement
© 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
Identifier
http://ceur-ws.org/Vol-2048/paper11.pdf
Source
17th Workshop on Computational Models of Natural Argument
Notes
timestamp: Wed, 17 Jan 2018 16:31:39 +0100 biburl: http://dblp.org/rec/bib/conf/icail/CocarascuT17 bibsource: dblp computer science bibliography, http://dblp.org
Start Date
2017-07-16
Coverage Spatial
London, UK