Closing the Gap Between Stochastic and Rule-based LFG Grammars

Developing large-scale deep grammars in a constraint-based framework such as Lexical Functional Grammar (LFG) is time-consuming and requires significant linguistic insight. Recently, treebank-based constraint-grammar acquisition approaches have been developed as an alternative to hand-crafting such resources. While treebank-based approaches are wide coverage and robust and achieve competitive evaluation results for many languages, the granularity of the linguistic analyses provided by treebank-based resources tends to be less fine-grained than what is offered by state-of-the-art hand- crafted grammars. This paper presents an approach to extend the English DCU LFG annotation algorithm with more detailed f-structure information to provide probabilistic treebank-based LFG grammars with rich feature information comparable to that implemented by the hand-crafted English XLE grammar, while maintaining the robustness and the coverage of treebank-based stochastic grammars.