The minutes and discussion regarding applications of WSD has been very
interesting, especially for those of us who didn't attend the workshop in
Barcelona. Thanks to all of you who have contributed to this!

At the risk of repeating what might have already been said at that event,
here are a few misc thoughts about WSD and applications.

Lexical sample word sense disambiguation seems most useful to me in terms
of studying the properties and behavior of a particular word or sense. I
personally find this fascinating, and seeing how you can figure out
which meaning of "line" or "train" was intended in a particular context
still seems like a very worthwhile pursuit. It's about understanding a
word, or a particular sort of feature, or the nature of a particular sense
inventory or sense distinction.

But as a practical matter, I can't think of applications based on lexical
sample disambiguation, simply because to achieve any coverage you end up
with thousands of classifiers, each of which know alot about a particular
word (but not how those words interact with each other). Training each
of those classifiers is hard and requires lots of data, and of course
there is the issue of fixing upon a sense inventory that might not be the
best choice for all applications.

The other significant potential downside to lexical sample style
disambiguation is that it can easily turn into an exercise in machine
learning methodology, where the end result are accuracy values in a
table, rather than contributing something to an application.  This isn't
all bad, in that word sense disambiguation has told us lots about machine
learning methodology  (merits of naive bayes, co-training, svms, decision
trees, etc). But it doesn't really lend itself to integration in
applications as best I can tell.

However, let's imagine for a moment that there was something like the
Brill Tagger for WSD - free code, easy to install, use, and  understand,
and that it performs all-words sense disambiguation reasonably well
(whatever that might mean).

I think then applications would come out of the woodwork. One example, I
was at AAAI this July (part of the reason why I wasn't at ACL) and I
talked to various folks who really seemed to want a system that
disambiguates all content words in text. Given the availability of such a
tool, they would have at least tried it out, and maybe some clever new
use of WSD would come from that. My own experience with some software
we've released is that people quickly discover ways to use it that I could
have never predicted, and I think reaching out to potential users of
WSD with tools they can use is really what we need to resolve this issue
of applications and WSD.

My conclusion? If we want to deal with applications, we need to focus
more on all-words disambiguation. In short, we need to have tools like
the Brill Tagger available for WSD. Maybe not perfect, maybe not even the
best possible result, but easy to use and try out.

Finally, I would be remiss if I didn't mention that I think data-driven
clustering approaches are a very important future directions. But that's
another topic. The point of this note is that I still think there is a
place for "traditional" WSD, but that we need to focus on getting our
tools and ideas in the hands of application developers so they can be the
ones to decide when and how to use WSD, and whether or not it helps.

Cordially,
Ted

PS To practice what I preach, one of my higher priorities these days is
to revamp our lexical sample SenseRelate system to handle all words
disambiguation  (http://senserelate.sourceforge.net)

--
Ted Pedersen
http://www.d.umn.edu/~tpederse


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