This paper addresses the task of semantic class learning by introducing a new methodology to identify the set of semantic classes underlying an aggregate of instances (i.e, a set of nominal phrases observed as a particular semantic role in a collection of text documents). The aim is to identify a set of semantically coherent (i.e., interpretable) and general enough classes capable of accurately describing the full extension that the set of instances is intended to represent. Thus, the set of

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... rned classes is then used to devise a generative model for entity categorization tasks such as semantic class induction. The proposed methods are completely unsupervised and rely on an (unlabeled) open-domain collection of text documents used as the source of background knowledge. We demonstrate our proposal on a collection of news stories. Specifically, we model the set of classes underlying the predicate arguments in a Proposition Store built from the news. The experiments carried out show significant improvements over a (baseline) generative model of entities based on latent classes that is defined by means of Hierarchical Dirichlet Processes.