Bootstrapped extraction of class attributes

Authors:
Joseph Reisinger;Marius Pasca
Affiliations:
The University of Texas at Austin, Austin, TX, USA;Google, Inc, Mountain View, CA, USA
Venue:
Proceedings of the 18th international conference on World wide web
Year:
2009

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Abstract

As an alternative to previous studies on extracting class attributes from unstructured text, which consider either Web documents or query logs as the source of textual data, A bootstrapped method extracts class attributes simultaneously from both sources, using a small set of seed attributes. The method improves extraction precision and also improves attribute relevance across 40 test classes.