> Anyway, I don't see anything in that paper that is worth saving. It old
> crap, we've been doing better for years, Rohit demonstrated that.

Well, we have actually not demonstrated better results than those
Stanford guys on word sense disambiguation or unsupervised
part-of-speech learning....  Maybe we can get better results than them
using the stuff you and Rohit were doing, I dunno...   i kinda doubt
it, but that's an empirical question...

> The missing next step is the dimensional reduction, and you suggest using
> linear matrix algos, but I don't see why these would be better than
> agglomerative clustering.  They seem to be harder to control, and gut
> instinct says they won't give good results.

Our instincts/intuitions seem to differ here.  But fortunately this is
a relatively straightforward empirical question...

My own experience and intuition is that agglomerative clustering is
crude and works pretty badly, and I think these NN techniques can do
better...

But we don't need to argue about this stuff....  I mean, the beauty of
this sort of work is that one has data and one can try different
algorithms and see what the results are like.   You've done this
excellent work building the first-phase MST parses, so now we can take
the data from these first-phase MST parses and apply various
category-learning and disambiguation techniques to the data, and see
what kind of results come out...

I'm not trying to convince you to try any category-learning or
disambiguation methods that don't match your intuition.   You can try
some methods based on your own instinct, and Ruiting and I will play
with some methods based on our own instincts, and we can compare
results....   If your instinct is right then Ruiting and I will have
wasted a little time experimenting with inferior techniques but we
will also have learned a bunch...

-- Ben

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