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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.
Number of Authors: 2
2011 (English)Conference 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

Open Access in DiVA

fulltext(160 kB)2 downloads
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File name FULLTEXT01.pdfFile size 160 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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SICS
Computer and Information Science

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ReferencesLink to record
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