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Plagiarism detection using semantic analysis

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dc.contributor.advisor Rhaman, Md. Khalilur
dc.contributor.author Shams, Khalid
dc.date.accessioned 2011-01-26T08:44:10Z
dc.date.available 2011-01-26T08:44:10Z
dc.date.copyright 2010
dc.date.issued 2010-04
dc.identifier.other ID 02201081
dc.identifier.uri http://hdl.handle.net/10361/741
dc.description This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2010.
dc.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 24).
dc.description.abstract Plagiarism in the sense of “theft of intellectual property” has been around for as long as humans have produced work of art and research. However, easy access to the Web, large databases, and telecommunication in general, has turned plagiarism into a serious problem for publishers, researchers and educational institutions. Plagiarism detection is a technique to find out the theft of scientific paper, literary works, source code etc. An existing method to find out similar documents is to use Self-Organizing Maps (SOMs)1. But there are some efficiency challenges like processing time arise in creating these maps. To facilitate recognition of plagiarism, Researchers2,3 at MIT used a set of low-level syntactic structures to evaluate content and expression in a document. However, we think only syntactic structures may not give optimal output in detecting plagiarism because it may not always detect the insight meaning. To detect plagiarism, our idea is to propose a synonym and antonym based framework to evaluate text similarity with respect to the similarity of content between the original and plagiarized document. Rather using low-level syntactic structures i.e. Context-free Grammar (CFG)4, synonymic features of sentences which we think will improve the overall combat against plagiarism. en_US
dc.description.statementofresponsibility Shams, Khalid
dc.description.statementofresponsibility 36 pages
dc.format.extent 36 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Computer science and engineering
dc.title Plagiarism detection using semantic analysis en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.description.degree B. Computer Science and Engineering


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