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190 Artificial Intelligence for the Internet of Everything

Fig. 11.1 Claim validity assessment pipeline.

The concept of automatically identifying argumentation schemes was
first discussed by Walton, Reed, and Macagno (2008). Ghosh, Muresan,
Wacholder, Aakhus, and Mitsui (2014)) investigate argumentation discourse
structure of a specific type of communication—online interaction threads.
Identifying argumentation in text is connected to the problem of identifying
truth, misinformation, and disinformation on the web (Galitsky, 2015;
Pendyala & Figueira, 2015; Pisarevskaya, Litvinova, & Litvinova, 2017).
Lawrence and Reed (2015) combined three types of argument structure
identification: linguistic features, topic changes, and machine learning.
To represent the linguistic features of text, we use the following sources:
(1) Rhetoric relations between the parts of the sentences, obtainedas a DT,and
(2) Speech acts and communicative actions, obtained as verbs from the Verb-
Net resource.
To assess the logical validity of an extracted argument, we apply the Defea-
sible Logic Program (DeLP) (Garcia & Simari, 2004), part of which is built
on the fly from facts and clauses extracted from these sources. We integrate
argumentation detection and validation components into a decision support
system that can be deployed, for example, in the CRM domain. To evaluate
our approach to extraction and reasoning for argumentation, we chose the
dispute resolution/customer complaint validation task because affective
argumentation analysis plays an essential role in it.

11.2 REPRESENTING ARGUMENTATIVE DISCOURSE

We start with a political domain and give an example of conflicting agents
providing their interpretation of certain events. These agents provide