Skip to main navigation Skip to search Skip to main content

A discriminative model for joint morphological disambiguation and dependency parsing

  • John Lee
  • , Jason Naradowsky
  • , David A. Smith

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Abstract

    Most previous studies of morphological disambiguation and dependency parsing have been pursued independently. Morphological taggers operate on n-grams and do not take into account syntactic relations; parsers use the "pipeline" approach, assuming that morphological information has been separately obtained. However, in morphologically-rich languages, there is often considerable interaction between morphology and syntax, such that neither can be disambiguated without the other. In this paper, we propose a discriminative model that jointly infers morphological properties and syntactic structures. In evaluations on various highly-inflected languages, this joint model outperforms both a baseline tagger in morphological disambiguation, and a pipeline parser in head selection. © 2011 Association for Computational Linguistics.
    Original languageEnglish
    Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
    Pages885-894
    Volume1
    Publication statusPublished - 2011
    Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
    Duration: 19 Jun 201124 Jun 2011

    Publication series

    Name
    Volume1

    Conference

    Conference49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
    PlaceUnited States
    CityPortland, OR
    Period19/06/1124/06/11

    Fingerprint

    Dive into the research topics of 'A discriminative model for joint morphological disambiguation and dependency parsing'. Together they form a unique fingerprint.

    Cite this