Inspecting the structural biases of dependency parsing algorithms

Yoav Goldberg, Michael Elhadad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

We propose the notion of a structural bias inherent in a parsing system with respect to the language it is aiming to parse. This structural bias characterizes the behaviour of a parsing system in terms of structures it tends to under- and over- produce. We propose a Boosting-based method for uncovering some of the structural bias inherent in parsing systems. We then apply our method to four English dependency parsers (an Arc-Eager and Arc-Standard transition-based parsers, and first- and second-order graph-based parsers). We show that all four parsers are biased with respect to the kind of annotation they are trained to parse. We present a detailed analysis of the biases that highlights specific differences and commonalities between the parsing systems, and improves our understanding of their strengths and weaknesses.

Original languageEnglish
Title of host publicationCoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages234-242
Number of pages9
ISBN (Print)9781932432831
StatePublished - 1 Dec 2010
Event14th Conference on Computational Natural Language Learning, CoNLL 2010 - Uppsala, Sweden
Duration: 15 Jul 201016 Jul 2010

Publication series

NameCoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference14th Conference on Computational Natural Language Learning, CoNLL 2010
Country/TerritorySweden
CityUppsala
Period15/07/1016/07/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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