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Absolutely, it is inadequate for modeling language, but then everything is! (My assertion is that so much contextual information is required to converse with h

Absolutely, it is inadequate for modeling language, but then everything is! (My assertion is that so much contextual information is required to converse with humans that one needs to build a proper AI in order to have unrestricted conversation. That or you have to reliably constrain conversation to a domain) The importance of systems like Montague Semantics are to kind of scope out what sort of thing we need to be able to model language in the abstract. Statistical NLP is definitely way better for building engineering systems that are deployed to accomplish particular tasks.

adimitrov 14y ago
Yes, just because something is incredibly hard, doesn't mean you have to stop pursuing it. We thought in the fifties and sixties that with rapidly increasing computing power, modelling natural would be within reach soon. Oh, how wrong we were. Natural language (and human thought for that matter, because ultimately, AI and NLP might be two facets of the same problem) is so much more complicated than we imagined. Researching human language and thought gives us insight not only sufficient to engineer interactive systems, but also to understand the human condition as a whole. Just take the entire discussion about rigid designators, and naming across possible worlds in intensional logics (read "Naming and Necessity" by Saul Kripke.) It is but one of the ways in which the need for a good formal approach to language resulted in an amazing philosophical discussion that isn't just about models, but our understanding of the world. Ultimately, natural language semantics can quickly transcend into deep philosophy. It sometimes takes me completely by surprise, actually :-)