Systems that automatically discover semantic classes have emerged in part to address the limitations of broad-coverage lexical resources such as WordNet and Cyc. The current state of the art discovers many semantic classes but fails to label their concepts. We propose an algorithm labeling semantic classes and for leveraging them to extract is-a relationships using a top-down approach.
REF have proposed an algorithm for labeling semantic classes, which can be viewed as a form of cluster.
Automatically Labeling Semantic Classes
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science