CIS Department Talk - March 19, 2007
The Department of Computer and Information Science & The Society of
Computer Science Present
Speaker: | Hui Fong, University of Illinois at Urbana Champagne
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Topic: | An Axiomatic Approach to Information Retrieval
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Date: | Monday March 19th, 2:30 pm |
Place: | John Mulcahy Hall, Room 138 |
Abstract:
With the explosive growth of online information, we have an urgent
need for powerful search engines to help manage and make use of all
the information. How effectively search engines, such as Google, help
us find useful information directly affects our productivity and
quality of life. The effectiveness of any search engine is mainly
determined by the underlying information retrieval model. A common
deficiency of existing retrieval models is that there is generally no
guarantee of the optimality of performance, and heavy parameter
tuning is always needed to achieve optimal performance on a
particular data set, which not only is labor-intensive, but also
provides no guarantee of optimality on future queries.
In this talk, I will present a novel axiomatic framework for
information retrieval. This new framework is fundamentally different
from all previous retrieval frameworks in that relevance of documents
to a query is captured with formalized retrieval constraints defined
at the level of terms. I will present specific research results that
demonstrate three benefits of such an axiomatic framework: (1) it can
predict the performance of a retrieval function analytically without
needing empirical experimentation; (2) it serves as a roadmap and
provides guidance for developing new effective retrieval functions;
and (3) it suggests a novel evaluation methodology that can diagnose
strengths and weaknesses of retrieval functions. The axiomatic
framework opens up a new promising direction in studying information
retrieval models. Using the framework, we have derived several new
retrieval functions that are more effective and robust than existing
retrieval functions. Moreover, the derived functions can be used in
any retrieval applications to improve search accuracy.
Bio:
Hui Fang is a Ph.D. candidate in Department of Computer Science at
University of Illinois at Urbana-Champaign, advised by Prof.
ChengXiang Zhai. She obtained her B.E. in Computer Science from
Tsinghua University in 2001 and her M.S. in Computer Science from
University of Illinois at Urbana-Champaign in 2004. Her primary
research interest is information retrieval, with focus on developing
effective and robust retrieval models. She is also interested in
bioinformatics, data mining and databases. She received the ACM SIGIR
2004 Best Paper Award for her work on information retrieval models.
For more information, contact:
Ms. Diane Roche (718) 817-4480; (roche@cis.fordham.edu)
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