Paper: Deep Dependencies From Context-Free Statistical Parsers: Correcting The Surface Dependency Approximation

ACL ID P04-1042
Title Deep Dependencies From Context-Free Statistical Parsers: Correcting The Surface Dependency Approximation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

We present a linguistically-motivated algorithm for recon- structing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. We use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. We find that our algo- rithm compares favorably with prior work on English using an existing evaluation metric, and also introduce and argue for a new dependency-based evaluation metric. By this new eval- uation metric our algorithm achieves 60% error reduction on gold-standard input trees and 5% error reduction on state-of- the-art machine-parsed input trees, when compared with the best previous work. We also present the first results on non- local dep...