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Experiments to investigate the utility of nearest neighbour metrics based on linguistically informed features for detecting textual plagiarism
RISE, Swedish ICT, SICS. IAM.
RISE, Swedish ICT, SICS. IAM.ORCID iD: 0000-0003-4042-4919
Number of Authors: 2
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Plagiarism detection is a challenge for linguistic models — most current implemented models use simple occurrence statistics for linguistic items. In this paper we report two experiments related to plagiarism detection where we use a model for distributional semantics and of sentence stylistics to compare sentence by sentence the likelihood of a text being partly plagiarised. The result of the comparison are displayed for visual inspection by a plagiarism assessor.

Place, publisher, year, edition, pages
2011, 11.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-23847OAI: oai:DiVA.org:ri-23847DiVA: diva2:1042925
Conference
NoDaLiDa'11 (Nordiska Datalingvistikdagarna)
Projects
DISTRESS
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2016-12-28Bibliographically approved

Open Access in DiVA

fulltext(160 kB)5 downloads
File information
File name FULLTEXT01.pdfFile size 160 kBChecksum SHA-512
84f95a3a0ece269121863ebce68ef44941533ba13fa6aec1dfbdb6c9f27771c095dec9d9d0ace01a63fdda6e703b6c66793d3e0836a39fc477f43226e405b07d
Type fulltextMimetype application/pdf

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Karlgren, Jussi
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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