Intrinsic plagiarism detection using latent semantic indexing and stylometry

Muna Alsallal; Rahat Iqbal; Saad Amin; Anne James

doi:10.1109/DeSE.2013.34

Intrinsic plagiarism detection using latent semantic indexing and stylometry

Muna Alsallal, Rahat Iqbal, Saad Amin, Anne James

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

17 Citations (Scopus)

Abstract

Plagiarism is growing increasingly for the last few years due to the rapid proliferation of information through the World Wide Web (WWW). In this paper, we present an integrated approach based on Latent Semantic Indexing (LSI) and Stylometry technique for intrinsic plagiarism detection. LSI is used for the term document matrix of dataset, whereas, stylometry is used for intrinsic approximation of human writing style. We have conducted a series of experiments to investigate the efficiency of dimensionality reduction (DR) parameter as the core for LSI technique in order to gain insights into its effects using a small corpus. Following that, we carried out comparative evaluation of our approach by using the LSI and Stylometry separately, and then applying them together. Our results show that the performance of the proposed approach was improved when an integrated approach consisting of LSI and stylometry was applied.

Original language	English
Title of host publication	Proceedings - 2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	145-150
Number of pages	6
ISBN (Electronic)	9781479952649
DOIs	https://doi.org/10.1109/DeSE.2013.34
Publication status	Published - 11 Feb 2013
Event	2013 6th International Conference on Developments in eSystems Engineering - Abu Dhabi, United Arab Emirates Duration: 16 Dec 2013 → 18 Dec 2013

Conference

Conference	2013 6th International Conference on Developments in eSystems Engineering
Abbreviated title	DeSE 2013
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	16/12/13 → 18/12/13

Keywords

Extrinsic plagiarism
Intrinsic plagiarism
Latent semantic indexing (LSI)
Plagiarism
Stylometry technique
Text misuse

ASJC Scopus subject areas

Control and Systems Engineering
Computer Networks and Communications
Computer Science Applications
Software

Access to Document

10.1109/DeSE.2013.34

Cite this

Intrinsic plagiarism detection using latent semantic indexing and stylometry. / Alsallal, Muna; Iqbal, Rahat; Amin, Saad et al.
Proceedings - 2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 145-150 7041107.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Alsallal, M, Iqbal, R, Amin, S & James, A 2013, Intrinsic plagiarism detection using latent semantic indexing and stylometry. in Proceedings - 2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013., 7041107, Institute of Electrical and Electronics Engineers Inc., pp. 145-150, 2013 6th International Conference on Developments in eSystems Engineering, Abu Dhabi, United Arab Emirates, 16/12/13. https://doi.org/10.1109/DeSE.2013.34

@inproceedings{81c885bfaa144425a7c1b4393dd97533,

title = "Intrinsic plagiarism detection using latent semantic indexing and stylometry",

abstract = "Plagiarism is growing increasingly for the last few years due to the rapid proliferation of information through the World Wide Web (WWW). In this paper, we present an integrated approach based on Latent Semantic Indexing (LSI) and Stylometry technique for intrinsic plagiarism detection. LSI is used for the term document matrix of dataset, whereas, stylometry is used for intrinsic approximation of human writing style. We have conducted a series of experiments to investigate the efficiency of dimensionality reduction (DR) parameter as the core for LSI technique in order to gain insights into its effects using a small corpus. Following that, we carried out comparative evaluation of our approach by using the LSI and Stylometry separately, and then applying them together. Our results show that the performance of the proposed approach was improved when an integrated approach consisting of LSI and stylometry was applied.",

keywords = "Extrinsic plagiarism, Intrinsic plagiarism, Latent semantic indexing (LSI), Plagiarism, Stylometry technique, Text misuse",

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

year = "2013",

month = feb,

day = "11",

doi = "10.1109/DeSE.2013.34",

language = "English",

pages = "145--150",

booktitle = "Proceedings - 2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013 ; Conference date: 16-12-2013 Through 18-12-2013",

}

TY - GEN

T1 - Intrinsic plagiarism detection using latent semantic indexing and stylometry

AU - Alsallal, Muna

AU - Iqbal, Rahat

AU - Amin, Saad

AU - James, Anne

PY - 2013/2/11

Y1 - 2013/2/11

N2 - Plagiarism is growing increasingly for the last few years due to the rapid proliferation of information through the World Wide Web (WWW). In this paper, we present an integrated approach based on Latent Semantic Indexing (LSI) and Stylometry technique for intrinsic plagiarism detection. LSI is used for the term document matrix of dataset, whereas, stylometry is used for intrinsic approximation of human writing style. We have conducted a series of experiments to investigate the efficiency of dimensionality reduction (DR) parameter as the core for LSI technique in order to gain insights into its effects using a small corpus. Following that, we carried out comparative evaluation of our approach by using the LSI and Stylometry separately, and then applying them together. Our results show that the performance of the proposed approach was improved when an integrated approach consisting of LSI and stylometry was applied.

AB - Plagiarism is growing increasingly for the last few years due to the rapid proliferation of information through the World Wide Web (WWW). In this paper, we present an integrated approach based on Latent Semantic Indexing (LSI) and Stylometry technique for intrinsic plagiarism detection. LSI is used for the term document matrix of dataset, whereas, stylometry is used for intrinsic approximation of human writing style. We have conducted a series of experiments to investigate the efficiency of dimensionality reduction (DR) parameter as the core for LSI technique in order to gain insights into its effects using a small corpus. Following that, we carried out comparative evaluation of our approach by using the LSI and Stylometry separately, and then applying them together. Our results show that the performance of the proposed approach was improved when an integrated approach consisting of LSI and stylometry was applied.

KW - Extrinsic plagiarism

KW - Intrinsic plagiarism

KW - Latent semantic indexing (LSI)

KW - Plagiarism

KW - Stylometry technique

KW - Text misuse

U2 - 10.1109/DeSE.2013.34

DO - 10.1109/DeSE.2013.34

M3 - Conference proceeding

AN - SCOPUS:84946688568

SP - 145

EP - 150

BT - Proceedings - 2013 6th International Conference on Developments in eSystems Engineering, DeSE 2013

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2013 6th International Conference on Developments in eSystems Engineering

Y2 - 16 December 2013 through 18 December 2013

ER -

Intrinsic plagiarism detection using latent semantic indexing and stylometry

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this