On Learning More Appropriate Selectional Restrictions
arXiv:cmp-lg/9502009
Abstract
We present some variations affecting the association measure and thresholding on a technique for learning Selectional Restrictions from on-line corpora. It uses a wide-coverage noun taxonomy and a statistical measure to generalize the appropriate semantic classes. Evaluation measures for the Selectional Restrictions learning task are discussed. Finally, an experimental evaluation of these variations is reported.
7 pages, LaTeX (eaclap.sty)