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>> So let's forget about computers for a second. We don't even know how humans process discourse... >> It seems more practical to do work on stuff that could a

>> So let's forget about computers for a second. We don't even know how humans process discourse... >> It seems more practical to do work on stuff that could actually benefit from machine learning, as opposed to trying to solve incredibly difficult (and mostly theoretical) problems like discourse analysis. The thing I am not sure about is how much it makes sense to attempt to create discourse analyses vs doing more "practical" work. The reason is that a complete and accurate discourse analysis seems nearly impossible. Having read through a few reference grammars and phonological analysis of various languages myself, it seems any rules linguists create are always wrought with copious exceptions and special cases (though certainly not all! There are examples of phonological rules that are very very regular across languages). Some of these analyses were extremely intricate. I got the impression that many of them suffered from the mistake of trying to prescribe patterns to data that does not have any patterns.

deleted 13y ago
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dvt 13y ago
I completely agree. Having worked with some AI systems in the past (mostly ANNs although I'd love to even begin to understand SVMs), I think that there's more "practicality" in just building robots/software that (based on various pattern analyses/etc/etc) figures out where a malignant tumor is, for example, rather than a robot that can understand coherence relations or what-have-you. So I don't fault the CS community from stepping away from linguistic puzzles and getting on with more useful stuff. After all, that's what engineering is. It looks like Levesque disagrees, but I'm not sure he's right.
- textminer 13y ago
  SVMs are lickety-split simple. You're drawing the best line/plane/hyperplane between some points. You can turn this into a nice convex optimization program, given some conditions. If this thing isn't fully separable, you can fudge it a little with some penalty terms, or you can cheaply project these points into some space where such a separation does exist. The hard part will always be your feature extraction, labeled data collection, and the parameter tuning for everything I just waved my hand at.

deleted 13y ago

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dvt 13y ago

I completely agree. Having worked with some AI systems in the past (mostly ANNs although I'd love to even begin to understand SVMs), I think that there's more "practicality" in just building robots/software that (based on various pattern analyses/etc/etc) figures out where a malignant tumor is, for example, rather than a robot that can understand coherence relations or what-have-you. So I don't fault the CS community from stepping away from linguistic puzzles and getting on with more useful stuff. After all, that's what engineering is. It looks like Levesque disagrees, but I'm not sure he's right.

textminer 13y ago
SVMs are lickety-split simple. You're drawing the best line/plane/hyperplane between some points. You can turn this into a nice convex optimization program, given some conditions. If this thing isn't fully separable, you can fudge it a little with some penalty terms, or you can cheaply project these points into some space where such a separation does exist. The hard part will always be your feature extraction, labeled data collection, and the parameter tuning for everything I just waved my hand at.