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Accessing Validity of Argumentation of Agents of the Internet of Everything 189

advise other customers to avoid particular financial services. Multiple affec-
tive argumentation patterns are used in complaints; the most frequent is an
intense description by a complainant of a deviation from what was expected,
according to common sense, to what actually happened. This pattern covers
both valid and invalid argumentation.
We select rhetoric structure theory (RST) (Mann & Thompson, 1988)as
a means to represent discourse features associated with logical and affective
argumentation. Nowadays, the performance of both rhetoric parsers and
argumentation reasoners has dramatically improved (Feng & Hirst, 2014).
Taking into account the discourse structure of conflicting dialogs, one
can judge the authenticity and validity of these dialogs in terms of its affective
argumentation. In this work we will evaluate the combined argument validity
assessment system that includes both the discourse structure extraction and
reasoning about it with the purpose of the validation of a complainant’s claim.
Either approach on argument detection from text or on reasoning about
formalized arguments has been undertaken (Galitsky & Pampapathi, 2003;
Symeonidis, Chatzidimitriou, Athanasiadis, & Mitkas, 2007), but not the
whole text assessment pipeline required for IoT systems.
Most of the modern techniques treat computational argumentation as
specific discourse structures and perform detection of arguments of various
sorts in text, such as classifying a text paragraph as argumentative or nonar-
gumentative (Moens et al., 2007). A number of systems recognize compo-
nents and structures of logical arguments (Sardianos, Katakis, Petasis, &
Karkaletsis, 2015; Stab & Gurevych, 2014). However, these systems do
not rely on discourse trees (DTs); they only extract arguments and do not
apply logical means to evaluate them. A broad corpus of research deals with
logical arguments irrespectively of how they may occur in natural language
(Bondarenko, Dung, Kowalski, & Toni, 1997). A number of studies
addressed argument quality in logic and argumentation theory (Damer,
2009; van Eemeren, Grootendorst, & Henkemans, 1996); however, the
number of systems that assess the validity of arguments in text is very limited
(Cabrio & Villata, 2012). This number is especially low for studies concern-
ing affective argumentation. Most argument mining systems are either clas-
sifiers that recognize certain forms of logical arguments in text, or reasoners
over the logical representation of arguments (Amgoud, Besnard, & Hunter,
2015). Conversely, in this project we intend to build the whole argumentation
pipeline, augmenting an argument extraction from text with its logical anal-
ysis (Fig. 11.1). This pipeline is necessary to deploy an argumentation anal-
ysis in a practical decision support system.