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204 Artificial Intelligence for the Internet of Everything

Otherwise, if they have the same part-of-speech, subject1^subject2¼<*,POS
(subject1), word2vecDistance(subject1^subject2)>.
If a part-of-speech is different, the generalization is an empty tuple. It
cannot be further generalized.
We combined Stanford NLP parsing, coreferences, entity extraction, DT
construction (discourse parser; Surdeanu, Hicks, & Valenzuela-Escarcega,
2016; Joty et al., 2013), VerbNet, and Tree Kernel builder into one system,
which is available at https://github.com/bgalitsky/relevance-based-on-
parse-trees.

11.5 ASSESSING VALIDITY OF EXTRACTED ARGUMENT
PATTERNS VIA DIALECTICAL ANALYSIS

To convince an addressee, a message needs to include an argument and
its structure needs to be valid. Once an argumentation structure extracted
from text is represented via CDT, we need to verify that the main point
(target claim) communicated by the author is not logically attacked by
her other claims. To assess the validity of the argumentation, a DeLP
approach is selected. It is an argumentative framework based on logic
programming (Alsinet, Chesn ˜evar, Godo, & Simari, 2008; Garcia &
Simari, 2004); we present an overview of the main concepts associated
with it.
A DeLP is a set of facts, strict rules Π of the form (A:-B), and a set of
defeasible rules Δ of the form A-<B, whose intended meaning is “if B is
the case, then usually A is also the case.” Let P¼(Π, Δ) be a DeLP program
and L a ground literal. Let us now build an example of a DeLP for legal

Fig. 11.4 An example of a Defeasible Logic Program for modeling category mapping.