An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Muna Alsallal; Rahat Iqbal; Anne James; Saad Amin; Vasile Palade

doi:10.1109/DeSE.2016.1

An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Muna Alsallal
, Rahat Iqbal
, Anne James
, Saad Amin
, Vasile Palade

Research output: Contribution to conference › Paper › peer-review

10 Citations (Scopus)

Abstract

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.

Original language	English
DOIs	https://doi.org/10.1109/DeSE.2016.1
Publication status	Published - 1 Sept 2016
Event	9th International Conference on Developments in eSystems Engineering - Liverpool and Leeds, United Kingdom Duration: 31 Aug 2016 → 2 Sept 2016 http://dese.org.uk/dese2016-conference/

Conference

Conference	9th International Conference on Developments in eSystems Engineering
Abbreviated title	DeSE
Country/Territory	United Kingdom
City	Liverpool and Leeds
Period	31/08/16 → 2/09/16
Internet address	http://dese.org.uk/dese2016-conference/

Keywords

Plagiarism
Semantics
Feature extraction
Tools
Support vector machines
Education
Writing

ASJC Scopus subject areas

Artificial Intelligence

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10.1109/DeSE.2016.1

Cite this

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title = "An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection",

abstract = "Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.",

keywords = "Plagiarism, Semantics, Feature extraction, Tools, Support vector machines, Education, Writing",

author = "Muna Alsallal and Rahat Iqbal and Anne James and Saad Amin and Vasile Palade",

year = "2016",

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doi = "10.1109/DeSE.2016.1",

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TY - CONF

T1 - An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

AU - Alsallal, Muna

AU - Iqbal, Rahat

AU - James, Anne

AU - Amin, Saad

AU - Palade, Vasile

PY - 2016/9/1

Y1 - 2016/9/1

N2 - Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.

AB - Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.

KW - Plagiarism

KW - Semantics

KW - Feature extraction

KW - Tools

KW - Support vector machines

KW - Education

KW - Writing

U2 - 10.1109/DeSE.2016.1

DO - 10.1109/DeSE.2016.1

M3 - Paper

T2 - 9th International Conference on Developments in eSystems Engineering

Y2 - 31 August 2016 through 2 September 2016

ER -

An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Abstract

Conference

Keywords

ASJC Scopus subject areas

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