Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank
Published 1 January 2006Open access
Ted Briscoe, John M. Carroll
Citations64
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TL;DR
It is demonstrated that a parser which is competitive in accuracy (without sacrificing processing speed) can be quickly tuned without reliance on large in-domain manually-constructed treebanks, making it more practical to use statistical parsers in applications that need access to aspects of predicate-argument structure.
Abstract
We evaluate the accuracy of an unlexicalized statistical parser, trained on 4K treebanked sentences from balanced data and tested on the PARC DepBank. We demonstrate that a parser which is competitive in accuracy (without sacrificing processing speed) can be quickly tuned without reliance on large in-domain manually-constructed treebanks. This makes it more practical to use statistical parsers in applications that need access to aspects of predicate-argument structure. The comparison of systems using DepBank is not straightforward, so we extend and validate DepBank and highlight a number of representation and scoring issues for relational evaluation schemes.
Keywords
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