Page 221 - Artificial Intelligence for the Internet of Everything

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statement of the article is that a certain agent “disallows” a particular kind of
evidence thereby attacking the main claim, rather than providing and back-
ing up this evidence. Instead of defeating a chemical attack claim, the article
builds a complex image of the conflicted mental states of the residents, Rus-
sian agents taking them to Brussels, the West, and a Middle-East expert (see
Fig. 11.3D).
Our other example of controversial news is a Trump–Russia link accu-
sation (BBC, 2018). For a long time the claim could not be confirmed, so the
story was repeated over and over again to maintain the reader’s expectation
that it would be instantiated one day. There is neither confirmation nor
rejection that the dossier exists and the goal of the author is to make the audi-
ence believe that such dossier exists without misrepresenting events. To
achieve this goal the author can attach a number of hypothetical statements
about the existing dossier to a variety of mental states to impress upon the
reader the authenticity and validity of the topic (see Fig. 11.3E).

11.4 RECOGNIZING COMMUNICATIVE DISCOURSE TREES
FOR ARGUMENTATION

Argumentation analysis needs a systematic approach to learn associated dis-
course structures. The features of CDTs could be represented in a numerical
space so that argumentation detection can be conducted; however, struc-
tural information on DTs would not be leveraged. Also, features of argu-
mentation can potentially be measured in terms of maximal common
sub-DTs, but such nearest-neighbor learning is computationally intensive
and too sensitive to errors in DT construction. Therefore a CDT-kernel
learning approach is selected, which applies support vector machine
(SVM) learning to the feature space of all sub-CDTs of the CDT for a given
text where an argument is being detected.
Tree kernel (TK) learning for strings, parse trees, and parse thickets is a
well-established research area nowadays. The CD-TK counts the number of
common sub-trees as the discourse similarity measure between two DTs.
A version of TK has been defined for discourse analysis ( Joty & Moschitti,
2014). Wang, Su, and Tan (2010) used the special form of TK for discourse-
relation recognition. In this study, we extend the TK definition for the
CDT, augmenting DT kernel by the information on CAs. TK-based
approaches are not very sensitive to errors in parsing (syntactic and rhetoric)
because erroneous sub-trees are mostly random and will unlikely be com-
mon among different elements of a training set.