Statistical filtering and subcategorization frame acquisition

Abstract

Research into the automatic acquisition of subcategorization frames (SCFs) from corpora is starting to produce large-scale computational lexicons which include valuable frequency information. However, the accuracy of the resulting lexicons shows room for improvement. One significant source of error lies in the statistical filtering used by some researchers to remove noise from automatically acquired subcategorization frames. In this paper, we compare three different approaches to filtering out spurious hypotheses. Two hypothesis tests perform poorly, compared to filtering frames on the basis of relative frequency. We discuss reasons for this and consider directions for future research.