Subject: Seminar Notice: Dr. Christopher W. Clifton (fwd)

From: "M. A. Saleh" <[email-masked]>
Date: Tue, 4 Apr 2000 19:51:28 -0400 (EDT)

---------- Forwarded message ----------
Date: Tue, 4 Apr 2000 16:03:02 -0400
From: Maureen Marano <[email-masked]>
Subject: Seminar Notice: Dr. Christopher W. Clifton
To: [email-masked]

        Department of Electrical Engineering and Computer Science

                                 Seminar

         TopCat:  Topic Identification using Data Mining in Text

                       Dr. Christopher W. Clifton
                          The MITRE Corporation
                               Bedford, MA

          When: Thursday, 4/6/00

          Where: 1-218 Center for Science and Technology

          Time: 2:30 p.m.

                                Abstract

In recent years, the amount of information available electronically has
exploded.  Data mining technology has provided breakthroughs in our
ability to analyze this information, but only for structured data. We
are lacking in our ability to exploit information contained in text.
Information retrieval systems have improved our ability to find the
"right" document in this morass, but what if the knowledge we want isn't
contained in a single document (or small set)?  I have been
investigating application of Data Mining technology to problems such as
link analysis that require gathering information from a large number of
documents. The term "Text Mining" has been used to describe these
problems, but the term is also being applied to standard information
retrieval.  I will discuss some of the problems to which the term "text
mining" has been applied, and review some of the work in this area.  A
recent breakthrough from my work has been TopCat: a topic identification
system that analyzes a news corpus to find major ongoing stories.  This
gives an overview of the corpus.  Topics are presented as a set of "key
players" (e.g., Iraq, Saddam Hussein, Richard Butler, U.N. Security
Council).  This is itself useful information, and also serves as a
starting point for further investigation (documents associated with a
topic, or a particular player in the topic, can be easily retrieved to
gain more information). This is one example of automated analysis of a
text corpus. Additional problems, such as link analysis, repeated event
sequences, and change detection will be presented.  Analysis of text
builds on the algorithms and techniques of data mining, but poses
additional challenges.  I will present some of the issues I have
identified, and outline research directions suggested by these issues.

This is joint work with Robert Cooley (University of Minnesota) and
Jason Rennie (MIT).
                               **********
                       Refreshments will be served

Dr. Christopher W. Clifton is a candidate for a faculty position in the
Department of Electrical Engineering and Computer Science.  He received
his Ph.D. in Computer Science from Princeton University in 1991.  His
current research interests are in data mining with an emphasis on text
mining and distributed data mining.  Dr. Clifton is presently a
Principal Scientist with the MITRE Corporation in Bedford, MA.