Smoothing of automatically generated selectional constraints
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TL;DR
An approach to automatically make suitable generalizations is reported on: using the co-occurrence data to compute a confusion matrix relating individual words, and then using the confusion matrix to smooth the original frequency data.
Abstract
Frequency information on co-occurrence patterns can be automatically collected from a syntactically analyzed corpus; this information can then serve as the basis for selectional constraints when analyzing new text from the same domain. Better coverage of the domain can be obtained by appropriate generalization of the specific word patterns which are collected. We report here on an approach to automatically make suitable generalizations: using the co-occurrence data to compute a confusion matrix relating individual words, and then using the confusion matrix to smooth the original frequency data.