Abstract

We describe a mechanism for automatically estimating frequencies of verb subcategorization frames in a large corpus. A tagged corpus is first partially parsed to identify noun phrases and then a regular grammar is used to estimate the appropriate subcategorization frame for each verb token in the corpus. In an experiment involving the identification of six fixed subcategorization frames, our current system showed more than 80% accuracy. In addition, a new statistical method enables the system to learn patterns of errors based on a set of training samples and substantially improves the accuracy of the frequency estimation.