Cross-framework parser stacking for data-driven dependency parsing

In this article, we present and evaluate an approach to the combination of a grammar-driven and a data-driven parser which exploits machine learning for the acquisition of syntactic analyses guided by both parsers. We show how conversion of LFG output to dependency representation allows for a technique of parser stacking, whereby the output of the grammar-driven parser supplies features for a data-driven dependency parser. We evaluate on English and German and show significant improvements in overall parse results stemming from the proposed dependency structure as well as other linguistic features derived from the grammars. Finally, we perform an application-oriented evaluation and explore the use of the stacked parsers as the basis for the projection of dependency annotation to a new language.