What do you want?

Philip's last question in class ("Tell me what you want!" to paraphrase) has spurred this drive-by post. I think, to be completely blunt, while the goal of this course appears to reside in presenting NLP techniques to linguists, I'm not sure that this means of presenting it really serves that purpose. The field of linguistics doesn't seem to work that way: "present me with a set of hammers, and I will find nails." NLP applications have worked that way, because they are engineering. Linguistics, particularly of the sort practised at UMD, is not.

What linguists have done is that they've found the nails first, and if you are a critic, you'd say that they've tried to bang the nails in with their fists. (If you are not a general critic of UMDian linguistics, you'd say that nails analogy is bogus, and I might actually agree with you, but that's the subject of a different post and maybe a different course.) Be that as it may, to accomplish the goal of making this class relevant to linguists, it really has to be approached from the opposite direction of an NLP applications course.

That is to say, it has to be shown that these techniques are in fact the correct set of hammers, and that means starting from the nail. The linguist's accusation against attempts at importing NLP concepts has always been that the hammer is always too big or too little. To reiterate, you can't really say, "Well, I have these techniques, they use data!!! Surely you can find something." The linguist will wonder, as linguists often do, why s/he needs more than s/he already has, and whether LSA is just too big a hammer.

Let's take syntax. A question that seems to have been a biggie for the past little while here at UMD is the "Agree" issue: do we need a special syntactic operation like "Agree"? It's still not clear that NLP techniques serve to resolve these high-level questions (eg, what operations exist and are minimally necessary). And if they don't, then one has to find lower-level questions, perhaps the mechanics of the syntax-semantics interface. But that requires NLP techniques that can map strings to at least the First Order Predicate Calculus in order to do anything at the sophistication that semanticists use.

Since data driven techniques have either sacrificed this approach out of expediency or found them impossible to support, it's hard to make the case for the involvement of NLP in at least those two domains of linguistic investigation (syntax and semantics). I would suggest that the place for these techniques lies in what these techniques do best: finding ways to compare/score the competing predictions of theories. That's a limited role, but potentially an important one, since it is often possible to come up with contradictory interpretations of the data even over a single language when dealing with the ad hoc manual inference that linguists often use. But this limited role will be the case at least until CL/NLP returns to the old questions of symbolic representations that have largely fallen by the wayside in favor of scale. I can't say much about psycholinguistics though, but they generally already seem to use statistics.