We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them poor models of mind and brain for language organization, as it interacts with infant biology, maturation, experience, unique principles, and natural law.

We consider the problem of semantic opposition; in particular, theproblem of determining adjective-verb opposition for transitive changeof state verbs and adjectivally modified grammatical objects. Semanticopposition problems of this type are a sub-case of the classic FrameProblem; the well-known problem of knowing what is preserved orchanged in the world as a result of some action or event. Bydefinition, grammatical objects of change of state verbs undergomodification. In cases where the object is adjectivally modified, theproblem reduces to determining whether the property denoted (...) by theadjective still holds true after the event denoted by the verb. Inthis paper, we evaluate the efficacy of WordNet, a network of conceptsorganized around linguistically relevant semantic relations includingantonymy, for this task. Test examples are drawn from the linguisticliterature. Results are analyzed in detail with a view towardsproviding feedback on the concept of a network as an appropriate modelof semantic relations for problems in semantic inference. (shrink)