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Encoding Sequential Information in Vector Space Models of Semantics: Comparing Holographic Reduced Representation and Random Permutation
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-5100-0535
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random permutations have been enlisted for this purpose in semantic models, these operations have never been systematically compared. In Experiment 1 we compare their storage capacity and probability of correct retrieval; in Experiments 2 and 3 we compare their performance on semantic tasks when integrated into existing models. We conclude that random permutations are a scalable alternative to circular convolution with several desirable properties.

Place, publisher, year, edition, pages
2010, 11. p. 865-870
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-23736OAI: oai:DiVA.org:ri-23736DiVA, id: diva2:1042813
Conference
Proceedings of the 32nd Annual Cognitive Science Society
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-21Bibliographically approved

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fulltext(249 kB)14 downloads
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File name FULLTEXT01.pdfFile size 249 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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Sahlgren, Magnus

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
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  • 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
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