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

Deep dependencies from context-free statistical parsers: correcting the surface dependency approximation Roger Levy Department of Linguistics Stanford University rog@stanford.edu Christopher D. Manning Departments of Computer Science and Linguistics Stanford University manning@cs.stanford.edu Abstract 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 dependency reconstruction for a language other than En- glish, comparing performance on English and German. Our new evaluation metric quantitatively corroborates the intuition that in a language with freer word order, the surface dependen- cies in context-free parse trees are a poorer approximation to underlying dependency structure. 1 Introduction While parsers are been used for other purposes, the primary motivation for syntactic parsing is as an aid to semantic interpretation, in pursuit of broader goals of natural language understanding. Propo- nents of traditional ‘deep’ or ‘precise’ approaches to syntax, such as GB, CCG, HPSG, LFG, or TAG, have argued that sophisticated grammatical for- malisms are essential to resolving various hidden re- lationships such as the source phrase of moved wh- phrases in questions and relativizations, or the con- troller of clauses without an overt subject. Knowl- edge of these hidden relationships is in turn es- sential to semantic interpretation of the kind prac- ticed in the semantic parsing (Gildea and Jurafsky, 2002) and QA (Pasca and Harabagiu, 2001) litera- tures. However, work in statistical parsing has for the most part put these needs aside, being content to recover surface context-free (CF) phrase structure trees. This perhaps reflects the fact that context-free phrase structure grammar (CFG) is in some sense at the the heart of the majority of both formal and computational syntactic research. Although, upon introducing it, Chomsky (1956) rejected CFG as an adequate framework for natural language descrip- tion, the majority of work in the last half century has used context-free structural descriptions and re- lated methodologies in one form or another as an important component of syntactic analysis. CFGs seem adequate to weakly generate almost all com- mon natural language structures, and also facilitate a transparent predicate-argument and/or semantic interpretation for the more basic ones (Gazdar et al., 1985). Nevertheless, despite their success in pro- viding surface phrase structure analyses, if statisti- cal parsers and the representations they produce do not provide a useful stepping stone to recovering the hidden relationships, they will ultimately come to be seen as a dead end, and work will necessarily re- turn to using richer …

Content truncated to 500 words