. NLP Semantics
There is general consensus as to what aspects of meaning must go into a representation, which one could say is roughly what is found in an extended case theory and its associated procedures (such as preference semantics); that description covers all systems up to, say, Pustejovksy's generative Lexicon, in fact everything short of model-theoretic semantics, which is still not generally accepted in NLP.
Neuro-Semantics stands out as both "enriched" NLP and "enriched" General Semantics. Returning to the sources of NLP, General Semantics, Bateson’s works in anthropology, schizophrenia, Levels of Learning, and cybernetics, MRI Institute, Cognitive Psychology (Miller, Galanter, Pribram), etc., we have sought to establish Neuro-Semantics on solid, scientific, and highly researched studies.
NLP, for a variety of reasons, has seemed to have received lots of negative and harmful Public Relations and General Semantics has seemed to locate itself in a small and isolated community. For these (and other reasons), we have sought to step aside just enough from NLP and GS so that we can both continue the adventure of modeling and engineering human excellence but not tied down to the limitations of the two source disciplines.
That early NLP modelling produced two NLP models which reflect the way we distort, delete and generalise information. The two models have come to be known as the meta model, and the Milton model. Meta modelling reconnects language with experience. Milton model languaging paces people's experience. Asking meta model questions elicits highly specified answers. Milton model languaging is artfully vague.