Frequency Estimation of Verb Subcategorization Frames Based on Syntactic and Multidimensional Statistical Analysis

Akira Ushioda, David A. Evans, Ted Gibson, Alex Waibel


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.
Anthology ID:
1993.iwpt-1.24
Volume:
Proceedings of the Third International Workshop on Parsing Technologies
Month:
August 10-13
Year:
1993
Address:
Tilburg, Netherlands and Durbuy, Belgium
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
309–318
Language:
URL:
https://www.aclweb.org/anthology/1993.iwpt-1.24
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/1993.iwpt-1.24.pdf