Publication
Large-Scale Corpus-Driven PCFG Approximation of an HPSG
Yi Zhang; Hans-Ulrich Krieger
In: 12th International Conference on Parsing Technologies. International Conference on Parsing Technologies (IWPT-2011), located at Hauptkonferenz, October 5-7, Dublin, Ireland, SigPARSE, 2011.
Abstract
We present a novel corpus-driven approach towards grammar
approximation for a linguistically deep Head-driven Phrase Structure
Grammar. With an unlexicalized probabilistic context-free grammar
obtained by Maximum Likelihood Estimate on a large-scale
automatically annotated corpus, we are able to achieve parsing
accuracy higher than the original HPSG-based model. Different ways
of enriching the annotations carried by the approximating PCFG are
proposed and compared. Comparison to the state-of-the-art
latent-variable PCFG shows that our approach is more suitable for
the grammar approximation task where training data can be acquired
automatically. The best approximating PCFG achieved ParsEval F$_1$
accuracy of 84.13\%. The high robustness of the PCFG suggests it is
a viable way of achieving full coverage parsing with the
hand-written deep linguistic grammars.