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Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also study the effect of using these vectors on the accuracy of dependency parsing in different languages versus using more complex parsing architectures.

Place, publisher, year, edition, pages
Linköping University , 2017. p. 21-28
Series
NEALT Proceedings Series, ISSN 1650-3686, E-ISSN 1650-3740
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics; Computer Science with specialization in Computer Communication
Identifiers
URN: urn:nbn:se:uu:diva-336722ISBN: 978-91-7685-601-7 (print)OAI: oai:DiVA.org:uu-336722DiVA, id: diva2:1166764
Conference
Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa
Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2018-01-13

Open Access in DiVA

fulltext(796 kB)26 downloads
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File name FULLTEXT01.pdfFile size 796 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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Basirat, AliNivre, Joakim
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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