An empirical study of semi-supervised structured conditional models for dependency parsing

Jun Suzuki, Hideki Isozaki, Xavier Carreras, Michael Collins

Research output: Contribution to conferencePaperpeer-review

59 Citations (Scopus)

Abstract

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semi-supervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first extension is to combine SS-SCMs with another semi-supervised approach, described in (Koo et al., 2008). The second extension is to apply the approach to second-order parsing models, such as those described in (Carreras, 2007), using a two-stage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Treebank for Czech. Our best results on test data in the above datasets achieve 93.79% parent-prediction accuracy for English, and 88.05% for Czech.

Original languageEnglish
Pages551-560
Number of pages10
DOIs
Publication statusPublished - 2009
Event2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore
Duration: 2009 Aug 62009 Aug 7

Other

Other2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
CountrySingapore
CitySingapore
Period09/8/609/8/7

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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