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2008

Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs

9 years 3 months ago
Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs
We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together, these two measures capture not only frequency of occurrence, but also cross-checking that the candidate occurs both near the class name and near other class members. We developed two algorithms that begin with just a class name and one seed instance and then automatically generate a ranked list of new class instances. We conducted experiments on four semantic classes and consistently achieved high accuracies.
Zornitsa Kozareva, Ellen Riloff, Eduard H. Hovy
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ACL
Authors Zornitsa Kozareva, Ellen Riloff, Eduard H. Hovy
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