It learns a set of concept clusters (i.e., categories), as well as a clustering over features (i.e., feature types), and a distribution over those feature clusters for each category (i.e., category-feature type associations).
the occurrences of a concept will be assigned a category, based on how similar the concept's feature types are compared to the feature types of all other potential categories.