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The Covert Helps Parse the Overt

Xun Zhang,Weiwei Sun,Xiaojun Wan

Published 2017 in Conference on Computational Natural Language Learning

ABSTRACT

This paper is concerned with whether deep syntactic information can help surface parsing, with a particular focus on empty categories. We design new algorithms to produce dependency trees in which empty elements are allowed, and evaluate the impact of information about empty category on parsing overt elements. Such information is helpful to reduce the approximation error in a structured parsing model, but increases the search space for inference and accordingly the estimation error. To deal with structure-based overfitting, we propose to integrate disambiguation models with and without empty elements, and perform structure regularization via joint decoding. Experiments on English and Chinese TreeBanks with different parsing models indicate that incorporating empty elements consistently improves surface parsing.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Conference on Computational Natural Language Learning

  • Publication date

    2017-08-01

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    DOI 10.18653/v1/K17-1035
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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