Deep Dependencies from Context-Free Statistical Parsers: Correcting the Surface Dependency Approximation

We present a linguistically-motivated algorithm for reconstructing 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 algorithm 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 evaluation metric our algorithm achieves 60% error reduction on gold-standard input trees and 5% error reduction on state-ofthe-art machine-parsed input trees, when compared with the best previous work. We also present the first results on nonlocal dependency reconstruction for a language other than English, comparing performance on English and German. Our new evaluation metric quantitatively corroborates the intuition that in a language with freer word ord...

• Type: Conference
• Year: 2004
• Where: ACL
• Authors: Roger Levy, Christopher D. Manning
• Keywords: ACL 2004, ACL 2007, Algorithm, Context-free Parse Trees, Nonlocal Dependency