Stochastic Attribute Value Grammars (SAVGs) provide an attractive framework for syntactic
analysis, because they allow the combination of linguistic sophistication with a principled …

We argue that some of the computational complexity associated with estimation of stochastic
attribute-value grammars can be reduced by training upon an informative subset of the full …

Probabilistic analogues of regular and context-free grammars are well-known in
computational linguistics, and currently the subject of intensive research. To date, however …

An attractive property of attribute-value grammars is their reversibility. Attribute-value
grammars are usually coupled with separate statistical components for parse selection and …

Probabilistic analogues of regular and context-free grammars are well-known in
computational linguistics, and currently the subject of intensive research. To date, however …

This paper reports on the use of two distinct evaluation metrics for assessing a stochastic
parsing model consisting of a broad-coverage Lexical-Functional Grammar (LFG), an …

In parse selection, the task is to select the correct syntactic analysis of a given sentence from
a set of parses generated by some other mechanism. On the basis of correctly labeled …

We present and evaluate an implemented statistical minimal parsing strategy exploiting DG
charateristics to permit fast, robust, deeplinguistic analysis of unrestricted text, and compare …

This paper reports some experiments that compare the accuracy and performance of two
stochastic parsing systems. The currently popular Collins parser is a shallow parser whose …

Extensible Dependency Grammar (XDG; Duchier and Debusmann (2001)) is a recently
developed dependency grammar formalism that allows the characterization of linguistic …