Paper: Statistical Bistratal Dependency Parsing

ACL ID D09-1059
Title Statistical Bistratal Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

We present an inexact search algorithm for the problem of predicting a two-layered dependency graph. The algorithm is based on a k-best version of the standard cubic- time search algorithm for projective de- pendency parsing, which is used as the backbone of a beam search procedure. This allows us to handle the complex non- local feature dependencies occurring in bistratal parsing if we model the interde- pendency between the two layers. We apply the algorithm to the syntactic– semantic dependency parsing task of the CoNLL-2008 Shared Task, and we obtain a competitive result equal to the highest published for a system that jointly learns syntactic and semantic structure.