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Summary: Amplifying Community Content Creation
with Mixed-Initiative Information Extraction
Raphael Hoffmann, Saleema Amershi, Kayur Patel, Fei Wu, James Fogarty, Daniel S. Weld
Computer Science & Engineering
DUB Group, University of Washington
Seattle, WA 98195
{raphaelh, samershi, kayur, wufei, jfogarty, weld}@cs.washington.edu
ABSTRACT
Although existing work has explored both information
extraction and community content creation, most research
has focused on them in isolation. In contrast, we see the
greatest leverage in the synergistic pairing of these
methods as two interlocking feedback cycles. This paper
explores the potential synergy promised if these cycles can
be made to accelerate each other by exploiting the same
edits to advance both community content creation and
learning-based information extraction. We examine our
proposed synergy in the context of Wikipedia infoboxes
and the Kylin information extraction system. After
developing and refining a set of interfaces to present the
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