A semantic class is a collection of items (words or phrases) which have semantically peer or sibling relationship. This paper studies the employment of topic models to automatically construct semantic classes, taking as the source data a collection of raw semantic classes (RASCs), which were extracted by applying predefined patterns to web pages. The primary requirement (and challenge) here is dealing with multi-membership: An item may belong to multiple semantic classes; and we need to discover as many as possible the different semantic classes the item belongs to. .
