Cluster Stopping Rules for Word Sense Discrimination
Guergana Savova, Terry M. Therneau, Christopher G. Chute · 2006
As text data becomes plentiful, unsupervised methods for Word Sense Disambiguation (WSD) become more viable. A problem encountered in applying WSD methods is finding the exact number of senses an ambiguity has in a training corpus collected in an automated manner. That number is not known a priori; rather it needs to be determined based on the data itself. We address that problem using cluster stopping methods. Such techniques have not previously applied to WSD. We implement the methods of