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.
A representative method is the hyponym pattern linkage graphs REF , which captures two properties associated with patternbased extractions: popularity (reflects the times that an instance could be discovered by other instances in the hyponym pattern) and productivity (reflects the times that an instance could lead to the discovery of other instances in the hyponym pattern).
Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Computer Science