Faculty Candidate COLLOQUIUM

Optimizing Information Extraction over Evolving Text Fei Chen University of Wisconsin-Madison Computer Science Faculty Candidate Tuesday, March 16, 2010 11:00 a.m. - 12:00 p.m. Fuller Labs 320 Abstract: Abstract: Information extraction (IE) programs automatically extract structured data that are embedded in text corpora. These extracted structured data can then be exploited by all kinds of applications such as structure queries and data mining. As such, IE is becoming increasingly important in managing text corpora. Most current IE solutions have considered only static text corpora, over which we typically apply IE programs only once. However, many real-world text corpora such as Wikipedia are evolving: documents can be added, deleted and modified. Therefore, to keep extracted information up to date, we often must apply IE programs repeatedly, to every corpus snapshot. How can we execute such repeated IE efficiently? In this talk, I will present efficient solutions for IE over evolving text. The underlying idea of these solutions is to recycle previous IE results, given that consecutive corpus snapshots often contain much overlapping text. I will first discuss Cyclex, a system that recycles for single IE programs. Cyclex models a small set of important properties shared by many practical IE programs to guarantee the correctness of recycling. Furthermore, it can efficiently recycle over large-scale text corpora by exploiting several database techniques such as cost-based optimization and a join-like recycling algorithm. Then I will talk about Delex, which builds on Cyclex and recycles for complex IE workflows that consist of multiple IE programs. Finally, I will conclude with future research directions in deploying and maintaining IE systems over large-scale text corpora. ______ Fei Chen is a PH.D. Candidate in the Database group at the University of Wisconsin-Madison. She is interested in building, deploying and maintaining efficient and robust systems that manage large-scale text corpora. In particular, her dissertation focuses on developing efficient information extraction solutions over large-scale evolving text. Host: Prof. Michael Gennert Refreshments will be served. Maintained by webmaster@cs.wpi.edu Last modified::March 10, 2010

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