Semantic Sequence Kin: A Method of Document Copy Detection
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Fingerprint and hash-based are common heuristic retrieval models.
“Fingerprint [115], [116] and hash-based [117], [118] are common heuristic retrieval models”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [117]
Cites
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096 · see citing works
Syntactic clustering of the Web
Broder, A., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00031-7 · Cited by 1,348 · see citing works
Copy detection mechanisms for digital documents
Brin, S., Davis, J., & García-Molina, H. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223855 · Cited by 479 · see citing works
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Si, A., Leong, H. V., & Lau, R. W. H. (1997) · ACM Symposium on Applied Computing · doi:10.1145/331697.335176 · Cited by 169 · see citing works
MatchDetectReveal: Finding Overlapping and Similar Digital Documents
Monostori, K., Zaslavsky, A., & Schmidt, H. W. (1999) · International Conference on Challenges of Information Technology Management in century · Cited by 36 · see citing works
Denning, P. J. (1995) · Communications of the ACM · doi:10.1145/219663.219675 · Cited by 19 · see citing works
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Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299 · see citing works
“On the other hand, SEM [14], [15] and FUZZY [16]–[19] methods incorporate different semantic features, thesaurus dictionaries, and lexical databases, such as WordNet.”
BackgroundMethodENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection ∗
Grozea, C., First, F., Gehl, C., & Popescu, M. (2009) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 123 · see citing works
Oberreuter, G., & Velásquez, J. D. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.082 · Cited by 77 · see citing works
“Bao et al. in Jun-Peng et al. (2003) and then in Bao et al. (2004) proposed using a semantic sequence kernel (SSK), and then inserting it into a traditional support vector machines (SVMs) formulation based on the structural risk minimization (SRM) (Boser, Guyon, & Vapnik, 1992; Vapnik, 1999)…”
MethodPlagiarism is Easy, but also Easy To Detect
Lyon, C., Barrett, R., & Malcolm, J. A. (2006) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 56 · see citing works
A Survey on Plagiarism Detection Systems
Bin-Habtoor, A. S., & Zaher, M. A. (2012) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2012.v4.447 · Cited by 42 · see citing works
Approaches for Intrinsic and External Plagiarism Detection - Notebook for PAN at CLEF 2011.
Oberreuter, G., L’Huillier, G., Ríos, S. A., & Velásquez, J. D. (2011) · Cited by 29 · see citing works
Discovering sentiment sequence within email data through trajectory representation
Liu, S., & Lee, I. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.026 · Cited by 19 · see citing works
Outlier-Based Approaches for Intrinsic and External Plagiarism Detection
Oberreuter, G., L’Huillier, G., Ríos, S. A., & Velásquez, J. D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23863-5_2 · Cited by 18 · see citing works
Comparing Different Text Similarity Methods
Bao, J., Lyon, C., Lane, P. C. R., Ji, W., & Malcolm, J. A. (2007) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 13 · see citing works
Self-citationCopy detection in Chinese documents using the Ferret: a report on experiments
Bao, J., Lyon, C., Lane, P. C. R., Ji, W., & Malcolm, J. A. (2006) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 9 · see citing works
Self-citationDocument plagiarism detection algorithm using semantic networks
Muftah, A. M., & Jabr, A. (2009) · Cited by 7 · see citing works
Work in Progress: Developing Arabic Plagiarism Detection Tool for E-Learning Systems
Alzahrani, S. M., Salim, N. B., & Alsofyani, M. M. (2009) · International Association of Computer Science and Information Technology · doi:10.1109/iacsit-sc.2009.22 · Cited by 6 · see citing works
Figure plagiarism detection based on textual features representation
Eisa, T. A. E., Salim, N. B., & Alzahrani, S. M. (2017) · ICT International Student Project Conference (ICT-ISPC) · doi:10.1109/ict-ispc.2017.8075305 · Cited by 4 · see citing works
“Since the threshold value was used to control the accuracy of detected plagiarised cases, choosing an optimal value was required to ensure a balance: the higher the threshold value, the higher the likelihood of plagiarism[24] .”
MethodAlzahrani, S. M., Salim, N. B., Kent, C. K., Binwahlan, M. S., & Suanmali, L. (2010) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2010.5687287 · Cited by 3 · see citing works
Texts Segmentation and Semantic Comparison: Method and Results of its Application
Rogozov, Y., Soto, J. G. B., Sviridov, A., Lipko, Y., Belikova, S., Belikov, A., Александрович, К. С., Shevchenko, O., Borisova, E., & Egorov, A. (2018) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct.2018.8588032 · Cited by 2 · see citing works
“There are also methods based on various characteristics of texts, for example, methods based on semantics for detecting plagiarism [6], [7] and for information search, as shown in [8].”
MethodSingh, M., Niranjan, A., Gupta, D., Bakshi, N. A., Mukherjee, A., & Goyal, P. (2017) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2017.7991589 · Cited by 2 · see citing works
An incremental algorithm of text clustering based on semantic sequences
Feng, Z., Junyi, S., & Bao, J. (2006) · Wuhan University Journal of Natural Sciences · doi:10.1007/bf02829263 · Cited by 1 · see citing works
Self-citationDiseño e Implementación de una Técnica para la Detección de Plagio en Documentos Digitales
Gallardo, O., & León, G. I. (2010)
Digital Media Copy Detection: Research Actuality and Prospect
Wan, Y., Wang, Y., Ji, S., Li-min, H., & Yuan, Q. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.655
“The result shows that SSK is excellent for detecting non-rewording plagiarism and valid even if documents are reworded to some extent[21].”
BackgroundContent-based Plagiarism Detection in Korean Document Using Ferret��s Trigram
Ahn, B.-R., Kim, W., & Kim, M. (2013) · Journal of Convergence Information Technology · doi:10.4156/jcit.vol8.issue11.83
“Semantic Sequence Kernel (SSK) detects plagiarism by finding the semantic sequences in a document and comparing them [3][4].”
BackgroundIsicas, F. D. C. F., & Aticas, Y. M. (2013)
Graphical Representation of Text Semantics
Fossoh, K. K. T., & Lo, D. C.-T. (2020) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3374135.3385326
Стенин, A. A., Pasko, V. P., Soldatova, M. A., & Дроздович, І. (2021) · Radio Electronics Computer Science Control · doi:10.15588/1607-3274-2021-3-14
“In particular, the main task of extracting data from the Web is to obtain certain pieces of information (fields) from HTML documents [6, 7].”
MethodA text annotation method based on semantic sequences
Bao, J., Lyon, C., & Lane, P. C. R. (2007) · University of Hertfordshire Research Archive (University of Hertfordshire)
Self-citationKorean Documents Copy Detection Based on Ferret
Ahn, B.-R., Kim, W.-G., Yu, W. Y., & Kim, M.-H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24728-6_60
- Authors
- Bao, J.-P., Shen, J.-Y., Liu, X.-D., Liu, H.-Y., & Zhang, X.-D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 529-538
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2152267356
- Cited by
- 25
- References
- 13
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 2 · 2017: 2 · 2013: 2 · 2012: 2
- FWCI
- 16.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- plagiarism detection, string kernels, semantic density, paraphrase, string matching
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