Acquisition of open-domain classes via intersective semantics

Marius Pasca

Abstract: A weakly-supervised method acquires fine-grained class labels that do not occur verbatim in the input data or underlying text collection. The method generates more specific class labels (gold mining companies listed on the toronto stock exchange) that capture the semantics of the underlying classes, out of pairs of input class labels (companies listed on the toronto stock exchange, gold mining companies) available for an instance (Golden Star Resources). When applied to Wikipedia articles and their categories, the method generates new categories for existing articles, and expands existing categories with additional articles.