Improving unsupervised dependency parsing with richer contexts and smoothing

William P. Headden*, Mark Johnson, David McClosky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

76 Citations (Scopus)

Abstract

Unsupervised grammar induction models tend to employ relatively simple models of syntax when compared to their supervised counterparts. Traditionally, the unsupervised models have been kept simple due to tractability and data sparsity concerns. In this paper, we introduce basic valence frames and lexical information into an unsupervised dependency grammar inducer and show how this additional information can be leveraged via smoothing. Our model produces state-of-the-art results on the task of unsupervised grammar induction, improving over the best previous work by almost 10 percentage points.

Original languageEnglish
Title of host publicationNAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages101-109
Number of pages9
ISBN (Print)9781932432411
Publication statusPublished - Jun 2009
Externally publishedYes
EventAnnual Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, NAACL HLT (10th : 2009) - Boulder, United States
Duration: 31 May 20095 Jun 2009

Other

OtherAnnual Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, NAACL HLT (10th : 2009)
CountryUnited States
CityBoulder
Period31/05/095/06/09

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