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Greedy Universal Dependency Parsing with Right Singular Word Vectors
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)
2016 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence matrix are used with the Stanford neural dependency parser to train parsing models for a limited number of languages in the corpus of universal dependencies. We show that the feature vector can help the parser to remain greedy and be as accurate as (or even more accurate than) some other greedy and non-greedy parsers.

Place, publisher, year, edition, pages
2016.
National Category
General Language Studies and Linguistics Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-310213OAI: oai:DiVA.org:uu-310213DiVA, id: diva2:1055648
Conference
Swedish Language Technology Conference (SLTC)
Available from: 2016-12-13 Created: 2016-12-13 Last updated: 2018-01-13Bibliographically approved

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fulltext(75 kB)153 downloads
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Type fulltextMimetype application/pdf

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Basirat, AliNivre, Joakim

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf