My first two days in San Jose were mostly occupied with being a panelist
at the AAAI Doctoral Consortium. This is an excellent event, where
graduate students come and present their work to a group of panelists (and
their fellow students) who then make comments about their particular
research topic, as well as issues relating to their presentation of the
work.

Here's the program, which includes a list of the panelists and the
students. You can also see extended abstracts describing the research of
each participating student.

http://www.csc.ncsu.edu/faculty/stamant/dc/dc04.html

The two students that I served as "main" panelist for were from Columbia
University in New York City, both doing very nice work in NLP. Their
names are Noemie Elhadad and Smaranda Muresan. Columbia is a great place
for NLP, with lots of interesting projects going on. They are both
involved in work that relates to the medical domain - Noemie's research
concerns how to "translate" medical text into a more readable form, while
Smaranda's objective is to develop techniques with which to build a
medical terminological knowledge base.

There was some other work that was also quite interesting and relevant to
things we do here at UMD, in particular that of Sugato Basu of the
University of Texas. He does work in semi-supervised clustering, and gave
us several nice pointers to work that might help us figure out how to stop
our own clustering techniques automatically (once they have found the
"right" number of clusters). In particular, he directed us to Tibshironi's
GAP statistic, which seems quite useful.

I'm going to put the URL to the Tibshironi paper here, just to make sure I
don't lose it. The answer to the question "how do you know how many clusters
to look for" has been something that has eluded me for some time now, so I
hope this or a similar technique helps.

http://www-stat.stanford.edu/~tibs/ftp/gap.pdf

Anyway, this is a nice example of serendipity at a conference. I wasn't
really expecting to learn about clustering at the Doctoral Consortium, but
as it turns out I learned quite a bit!

Anyway, the Doctoral Consortium is a fun event, in that it's a fairly
small number of students and panelists, and after a little while you get
to know each other reasonably well. In addition, the atmosphere is more
friendly and supportive than one might find in a conference presentation
setting, where the crowd is much larger and sometimes not as interested
in you as an individual. There is plenty of discussion after each
student presentation, and I think that might be the best part of the
experience for everyone. While the talks themselves are interesting,
sometimes the discussion thereafter really helps you to understand
what someone is doing (and why). Then, after the first day of talks, we
all went out to dinner at a very nice restaurant in San Jose. :)

The Doctoral Consortium happens every year at AAAI, and you should
participate in this if you are a PhD student in an AI related area (like
NLP). Usually you do this after you have selected a topic for your
dissertation research, but before you have really started writing the
dissertation. This is so you can (hopefully) benefit from the feedback you
get at the event, and thereby improve your dissertation. AAAI is very
generous with Doctoral Consortium participants, they usually receive a
travel grant that more than covers the cost of their trip to AAAI. (This
was true this year I think, and it was also true in 1997 when I was a
participant.)

Finally, you can see my 1997 Doctoral Consortium abstract here:

http://www.d.umn.edu/~tpederse/pubs.html

Look for this (only in ps format!)

Knowledge Lean Word Sense Disambiguation (Pedersen) - Appears in the
Proceedings of the Fourteenth National Conference on Artificial
Intelligence (AAAI-97), July 27-31, 1997, Providence, RI (Doctoral
Consortium)

You'll see that my interests haven't evolved all that much. :)

Ted

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


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