COLLOQUIUM
Department of Computer Science and Engineering
University of South Carolina
 
Topic Detection and Tracking Using
Nonnegative Matrix and Tensor Factorizations

Michael W. Berry

Department of Electrical Engineering and Computer Science
University of Tennessee

Date: January 24, 2008
Time: 1400-1500
Place: 300 Main B103

Abstract

Automated approaches for the identification and clustering of semantic
features or topics are highly desired for text mining applications.
Using a low rank non-negative matrix factorization (NNMF) algorithm to
retain natural data non-negativity, we eliminate the need to use
subtractive basis vector and encoding calculations present in
techniques such as principal component analysis for semantic feature
abstraction.  Using non-negative tensor factorization (NNTF), temporal
and semantic proximity can be exploited to enable tracking of focused
discussions as well as latent (unknown) communication
patterns. Demonstrations of NNMF and NNTF algorithms for topic (or
discussion) detection and tracking using the Enron Email Collection
and documents from the Airline Safety Reporting System (ASRS) are
provided.

Michael W. Berry holds the title of Full Professor and Coordinator for
Computer Science in the newly formed Department of Electrical
Engineering and Computer Science at the University of Tennessee,
Knoxville. He received the BS degree in Mathematics from the
University of Georgia in 1981 and the MS degree in Applied Mathematics
from North Carolina State University in 1983.  He worked in the
Communications Product Division of IBM in Raleigh, NC for about one
year before accepting a research staff position in the Center for
Supercomputing Research and Development at the University of Illinois
at Urbana-Champaign.  In 1990, he received the PhD degree in Computer
Science from the University of Illinois at
Urbana-Champaign. Prof. Berry is the co-author of Templates for the
Solution of Linear Systems: Building Blocks for Iterative Methods
(SIAM, 1994) and Understanding Search Engines: Mathematical Modeling
and Text Retrieval, Second Edition (SIAM, 2005) and editor of
Computational Information Retrieval (SIAM, 2001), Survey of Text
Mining: Clustering, Classification, and Retrieval (Springer, 2003,
2007), and Lecture Notes in Data Mining (World Scientific, 2006).  He
has published over 100 refereed journal and conference
publications. His research interests include information retrieval,
data and text mining, computational ecology, bioinformatics, and
parallel computing.  Prof. Berry is currently supported by grants and
contracts from the National Science Foundation, National Institutes of
Health, and the National Aeronautics and Space Administration..