Regarding NLP - I have always been suspicous about statistical methods of NLP, it is something like subsymbolic methods of neural networks. Such subsymbolic methods require other science to make explicit inference (about results, about argumentation) possible - there is connection science for neural networks but I don't know about similar tool for statistics. My ideal is NLP along lines of article "On Deep Computational Formalization of Natural Language" (available via Google search) and I wonder why this path has not been pursued so far. Lack of developed suitable logic (deontic event calculus in this case) is one explanation. There is clearly need for universal logic (as considered by Springer journal Logica Universalis) and I guess that categorical logic may become such logic - it already formalizes predicate and modal logics and similar formalization of probabilistic and adaptable logic (Strasser) (nonmonotonic) may be discovered in future (I hope, though I have no idea about direction how this can be done. Coalgebraic logic unifies modal and probabilistic logics but I had had hard time understanding it). Then it wil be possible to do formalization of natural language (in all its modes - starting from scientific reasoning and ending with emotional utterances) in such way.
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