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Combining Strategies for Extracting Relations from Text Collections

Agichtein, Eugene; Eskin, Eleazar; Gravano, Luis

Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or for running data mining tasks. Our Snowball system extracts these relations from document collections starting with only a handful of user-provided example tuples. Based on these tuples, Snowball generates patterns that are used, in turn, to find more tuples. In this paper we introduce a new pattern and tuple generation scheme for Snowball, with different strengths and weaknesses than those of our original system. We also show preliminary results on how we can combine the two versions of Snowball to extract tuples more accurately.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-006-00
Published Here
April 22, 2011
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