This paper presents experiments which combine a grammar-driven and a datadriven parser. We show how the conversion of LFG output to dependency representation allows for a techniqu...
This paper studies the computational complexity of disambiguation under probabilistic tree-grammars as in (Bod, 1992; Schabes and Waters, 1993). It presents a proof that the follo...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
Scannerless generalized parsing techniques allow parsers to be derived directly from unified, declarative specifications. Unfortunately, in order to uniquely parse existing progra...
We describe a bidirectional framework for natural language parsing and generation, using a typedfeatureformalismand an HPSG-based grammar with a parser and generator derived from ...