ML p(r)ior | On Learning More Appropriate Selectional Restrictions
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On Learning More Appropriate Selectional Restrictions

1995-02-09
9502009 | cmp-lg
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.
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