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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.ORCID iD: 0000-0003-4042-4919
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
URN: urn:nbn:se:ri:diva-23847OAI: diva2:1042925
NoDaLiDa'11 (Nordiska Datalingvistikdagarna)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2016-12-28

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Karlgren, Jussi
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