Paper: Using Semantically Motivated Estimates To Help Subcategorization Acquisition

ACL ID W00-1327
Title Using Semantically Motivated Estimates To Help Subcategorization Acquisition
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000
Authors

Research into the automatic acquisition of subcategorization frames from corpora is starting to produce large-scale computational lexicons which include valuable frequency in- formation. However, the accuracy of the resulting lexicons shows room for improve- ment. One source of error lies in the lack of accurate back-off estimates for subcatego- rization frames, delimiting the performance of statistical techniques frequently employed in verbal acquisition. In this paper, we propose a method of obtaining more accu- rate, semantically motivated back-off esti- mates, demonstrate how these estimates can be used to improve the learning of subcatego- rization frames, and discuss using the method to benefit large-scale lexical acquisition.