Date: Sun, 30 Jul 89 11:59 EST
From: 30-Jul-1989 1158 <LEWIS@cs.umass.EDU>
Subject: A Challenge Less Grand

   I agree with Pazzani and Tadepalli that the state of NLP technology is
insufficient to do what we'd really like, which is to learn form text the kind
of knowledge bases that will support powerful inference. However, I'll argue
that there are currently attackable and interesting learning issues related to
natural language text.

   In particular, information retrieval (IR) researchers have long applied
supervised and unsupervised learning techniques, mostly from pattern
recognition, to text retrieval. In recent years, NLP, knowledge-based, and
plausible inference techniques have started to be applied to IR (see recent
SIGIR proceedings), resulting in much more complex text representations. Making
effective use of these new representations will undoubtedly require different
learning approaches. Text retrieval has many advantages as a domain:

   --It is an extremely difficult and decidedly non-toy problem in
which there is great real-world interest.
   --There are methods in wide operational use which achieve a
reasonable, if far-from-perfect, level of performance, giving a
nontrivial standard of comparison.
   --There is a long history of careful evaluation in IR and a number
of standard test collections.
   --It's possible that even partial and errorful NLP techniques will
produce text representations that have a significant potential to
improve IR performance, especially if combined with effective learning
methods.

    So I propose IR as a slightly less grand, but more tractable challenge.
I'll contact David Aha to see about getting some of the standard IR collections
into the UCI repository, and I'd like to encourage machine learning researchers
to submit papers for the 1990 AAAI Spring Symposium on Text-Based Intelligent
Systems.       --Dave 

David D. Lewis                                     ph. 413-545-0728 
Information Retrieval Laboratory                   BITNET: lewis@umass
Computer and Information Science (COINS) Dept.     ARPA/MIL/CS/INTERnet:
University of Massachusetts, Amherst               lewis@cs.umass.edu 
Amherst, MA  01003                                        
USA                          UUCP: ...!uunet!cs.umass.edu!lewis@uunet.uu.net