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Argumentation meets Natural Language Processing: results achieved and open challenges| old_uid | 14629 |
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| title | Argumentation meets Natural Language Processing: results achieved and open challenges |
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| start_date | 2014/11/14 |
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| schedule | 11h-12h30 |
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| online | no |
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| summary | In this talk we will present existing approaches coupling
Argumentation Theory and Natural Language Processing, and then we will
present our contributions in that area, highlighting the remaining open challenges.
In order to cut in on a debate on the web, the participants need first
to evaluate the opinions of the other users to detect whether they are
in favor or against the debated issue. Bipolar argumentation proposes
algorithms and semantics to evaluate the set of accepted arguments,
given the support and the attack relations among them. Two main problems
arise. First, an automated framework to detect the relations among the
arguments represented by the natural language formulation of the users’
opinions is needed. Our talk addresses this open issue by proposing and
evaluating the use of natural language techniques to identify the
arguments and their relations. In particular, we adopt the textual
entailment approach, a generic framework for applied semantics, where
linguistic objects are mapped by means of semantic inferences at a
textual level. Textual entailment is then coupled together with an
abstract bipolar argumentation system which allows to identify the
arguments that are accepted in the considered online debate. Second, we
address the problem of studying and comparing the different proposals
put forward for modeling the support relation. The emerging scenario
shows that there is not a unique interpretation of the support relation.
In particular, different combinations of additional attacks among the
arguments involved in a support relation are proposed. We provide a
natural language account of the notion of support based on online
debates, by discussing and evaluating the support relation among
arguments with respect to the more specific notion of textual entailment
in the natural language processing field. |
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| responsibles | Candito |
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