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Computational Linguistics

Paola Merlo, Editor
March 2011, Vol. 37, No. 1, Pages 197-230.
(doi: 10.1162/coli_a_00039)
© 2011 Association for Computational Linguistics
Analyzing and Integrating Dependency Parsers
Article PDF (498.43 KB)
Abstract

There has been a rapid increase in the volume of research on data-driven dependency parsers in the past five years. This increase has been driven by the availability of treebanks in a wide variety of languages—due in large part to the CoNLL shared tasks—as well as the straightforward mechanisms by which dependency theories of syntax can encode complex phenomena in free word order languages. In this article, our aim is to take a step back and analyze the progress that has been made through an analysis of the two predominant paradigms for data-driven dependency parsing, which are often called graph-based and transition-based dependency parsing. Our analysis covers both theoretical and empirical aspects and sheds light on the kinds of errors each type of parser makes and how they relate to theoretical expectations. Using these observations, we present an integrated system based on a stacking learning framework and show that such a system can learn to overcome the shortcomings of each non-integrated system.