Yeah, what Asad said

I agree wholeheartedly with everything Asad said.

It's often mentioned that NLP turned away from "the old questions of symbolic representations" at the same time as there was relatively suddenly a huge increase in available electronic data (eg. the web) and in computing power to process it. But to be more precise, what we got a lot more of was electronic data about which words appear next to which other words, and linguists have long had a strong hunch that these aren't actually the data which most relevantly characterise a sentence.

It turned out that suddenly the results of the "old methods" (i.e. combining our best theory of which relationships really matter in a sentence, with the negligible data available about these relationships) were inferior to those of the "new methods" (i.e. combining a theory of which relationships really matter which was known to be not-quite-right, with the bucketloads of data available about those), and all the engineers flocked to these new methods, because they worked better. (Who would blame them?!) But I don't think this makes the theory that was known to be not-quite-right any better than we thought.

It might be argued that analysing the "which words are next to which other words" data as NLPers do is relevant for the language acquisition problem that linguists worry about, because this is the data that is available to human infants. I think this is a serious case of missing the point, however, because the infant comes equipped with "the right theory" (by definition) of which relationships really matter, so he/she is not in the same position as the engineers. (For subproblems at the phonological/morphological level, the situation might be more analogous, but I'm thinking more about sentence-level properties mainly.)

An interesting article on this topic, that Philip has mentioned a couple of times in class, is this one by Steven Abney:
http://www.ling.umd.edu/locker/ComputationalLearning/Abney95c.pdf
As may well be clear from the above, I happen to disagree with almost every interesting claim in there, but it does at least get you thinking. Some may remember rather lively discussions arising from this paper in last year's seminar.