Yes, we need a logic-based approach rather than a statistical one for NLP if you want to incorporate things like attention and memory. The full range of non-classical logics should be looked at, including Modal, Fuzzy etc.
We need to extend the logic-based approach to deal with reasoning under uncertainty, so many-valued logic is needed. Also, we need the logic to be able to model discrete, dynamical systems, so we need to look at Temporal Logic, Situation Calculus , Event Calculus etc etc. You can call all this 'Computational Logic'; it may actually be more general than probability theory.
See my wiki-book listing the central concepts of 'Computational Logic' here:
https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality...
Functional programming is best for handling the logic-based approach, for example Haskell. Functional programming languages work at a higher-level than ordinary imperative and procedural programming, since functional programming deals with the manipulation of knowledge itself rather states of the computer.
I'd also look closely at the pure mathematical origins of computational logic, including classical mathematical logic and category theory. Type theory (a constructive form of category theory) looks like it forms the basis for language rules, thus making it ideal for NLP.
I conjecture that an extension of computational logic leads to a solution to machine psychology (inc. NLP), analogous to how machine learning can be viewed as an extension of probability and statistics.
Probability&Stats >>> Machine Learning
By analogy,
Computational Logic >>> Machine Psychology (inc. NLP)
We need to extend the logic-based approach to deal with reasoning under uncertainty, so many-valued logic is needed. Also, we need the logic to be able to model discrete, dynamical systems, so we need to look at Temporal Logic, Situation Calculus , Event Calculus etc etc. You can call all this 'Computational Logic'; it may actually be more general than probability theory.
See my wiki-book listing the central concepts of 'Computational Logic' here:
https://en.wikipedia.org/wiki/User:Zarzuelazen/Books/Reality...
Functional programming is best for handling the logic-based approach, for example Haskell. Functional programming languages work at a higher-level than ordinary imperative and procedural programming, since functional programming deals with the manipulation of knowledge itself rather states of the computer.
I'd also look closely at the pure mathematical origins of computational logic, including classical mathematical logic and category theory. Type theory (a constructive form of category theory) looks like it forms the basis for language rules, thus making it ideal for NLP.
I conjecture that an extension of computational logic leads to a solution to machine psychology (inc. NLP), analogous to how machine learning can be viewed as an extension of probability and statistics.
Probability&Stats >>> Machine Learning
By analogy,
Computational Logic >>> Machine Psychology (inc. NLP)