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I browsed the table of content of Cann's Formal Semantics. Cann's book is for linguistics, and am I right that it is helpful for computational linguistics and natural language processing?

But it also seems to me that the approaches to NLP have been predominantly based on statistics and machine learning in applications (and maybe also research), which I think is completely unrelated to formal semantics?

So is formal semantics still useful for computational linguistics and NLP in applications and in research?

  • If not, what specific statistics and machine learning approaches have become successful replacement of formal semantics?

  • If yes, any actual implementations (or worked out suggestions for possible implementations) of formal semantics being used in computational linguistics and NLP?

Thanks.

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    Your question currently has close votes for being opinion based; but perhaps it can be made fully objectively answerable if you instead ask whether there are any actual implementations (or worked out suggestions for possible implementations) of formal semantics being used in NLP.
    – lemontree
    Aug 19 '20 at 14:50
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Yes, it’s useful. Formal semantics can serve as a basis for the stochastic methods. There are many approaches, let me just mention one — abductive parsing and interpretation. It’s based on formal semantics but the algorithms for analysing text are stochastic. IBM Watson is an example of a system based on formal semantics.

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