Unlexicalized dependency parser for variable word order languages based on local contextual pattern

Hoojung Chung, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

We investigate the effect of unlexicalization in a dependency parser for variable word order languages and propose an unlexicalized parser which can utilize some contextual information in order to achieve performance comparable to that of lexicalized parsers. Unlexicalization of an early dependency parser makes performance decrease by 3.6%. However, when we modify the unlexicalized parser into the one which can consider additional contextual information, the parser performs better than some lexicalized dependency parsers, while it requires simpler smoothing processes, less time and space for parsing.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlexander Gelbukh
PublisherSpringer Verlag
Pages112-123
Number of pages12
ISBN (Print)3540210067, 9783540210061
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2945
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chung, H., & Rim, H. C. (2004). Unlexicalized dependency parser for variable word order languages based on local contextual pattern. In A. Gelbukh (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 112-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2945). Springer Verlag. https://doi.org/10.1007/978-3-540-24630-5_14