Plagiarism Detection Avoidance Methods and Countermeasures

Plagiarism is a major problem that educators face in the information age. Today\u27s plagiarist has a near limitless supply of well-written articles via the internet. Due to the scale of the problem, detecting plagiarism has now become the domain of the computer scientist rather than the educator. With the use of computers, documents can be conveniently scanned into a plagiarism detection system that references public web pages, academic journals, and even previous students\u27 papers, acting as an all-seeing eye. However, plagiarists can overcome these digital content detection systems with the use of clever masking and substitutions techniques. These systems cost universities tens of thousands of dollars, and also infringe upon intellectual property ownership rights without the informed consent of individual students. In this work, we examine the efficacy of commercial plagiarism detection systems when used against some selected masking techniques, and then present a simple countermeasure to combat the aforementioned detection avoidance technique

Mining Social Science Publications for Survey Variables

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline

Plagiarism meets paraphrasing: insights for the new generation in automatic plagiarism detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyse the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource which uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analysed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarising, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analysed, providing critical insights for the improvement of automatic plagiarism detection systems

A resource-light method for cross-lingual semantic textual similarity

[EN] Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. (C) 2017 Published by Elsevier B.V.Part of the work presented in this article was performed during second author's research visit to the University of Mannheim, supported by Contact Fellowship awarded by the DAAD scholarship program "STIBET Doktoranden". The research of the last author has been carried out in the framework of the SomEMBED project (TIN2015-71147-C2-1-P). Furthermore, this work was partially funded by the Junior-professor funding programme of the Ministry of Science, Research and the Arts of the state of Baden-Wurttemberg (project "Deep semantic models for high-end NLP application").Glavas, G.; Franco-Salvador, M.; Ponzetto, SP.; Rosso, P. (2018). A resource-light method for cross-lingual semantic textual similarity. Knowledge-Based Systems. 143:1-9. https://doi.org/10.1016/j.knosys.2017.11.041S1914

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of F1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection

G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text

Measuring Sentences Similarity Based on Discourse Representation Structure

The problem of measuring similarity between sentences is crucial for many applications in Natural Language Processing (NLP). Most of the proposed approaches depend on similarity of words in sentences. This research considers semantic relations between words in calculating sentence similarity. This paper uses Discourse Representation Structure (DRS) of natural language sentences to measure similarity. DRS captures the structure and semantic information of sentences. Moreover, the estimation of similarity between sentences depends on semantic coverage of relations of the first sentence in the other sentence. Experiments show that exploiting structural information achieves better results than traditional word-to-word approaches. Moreover, the proposed method outperforms similar approaches on a standard benchmark dataset

Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection

[EN] Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.We would like to thank the people who participated in the annotation of the P4P corpus, Horacio Rodriguez for his helpful advice as experienced researcher, and the reviewers of this contribution for their valuable comments to improve this article. This research work was partially carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme. The research leading to these results received funding from the EU FP7 Programme 2007-2013 (grant no. 246016), the MICINN projects TEXT-ENTERPRISE 2.0 and TEXT-KNOWLEDGE 2.0 (TIN2009-13391), the EC WIQ-EI IRSES project (grant no. 269180), and the FP7 Marie Curie People Programme. The research work of A. Barron-Cedeno and M. Vila was financed by the CONACyT-Mexico 192021 grant and the MECD-Spain FPU AP2008-02185 grant, respectively. The research work of A. Barron-Cedeno was partially done in the framework of his Ph.D. at the Universitat Politecnica de Valencia.Barrón Cedeño, LA.; Vila, M.; Martí, MA.; Rosso, P. (2013). Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Computational Linguistics. 39(4):917-947. https://doi.org/10.1162/COLI_a_00153S917947394Barzilay, Regina. 2003. Information Fusion for Multidocument Summarization: Paraphrasing and Generation. Ph.D. thesis, Columbia University, New York.Barzilay, R., & Lee, L. (2003). Learning to paraphrase. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL ’03. doi:10.3115/1073445.1073448Barzilay, Regina and Kathleen R. McKeown. 2001. Extracting paraphrases from a parallel corpus. In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL 2001), pages 50–57, Toulouse.Barzilay, R., McKeown, K. R., & Elhadad, M. (1999). Information fusion in the context of multi-document summarization. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -. doi:10.3115/1034678.1034760Bhagat, Rahul. 2009. Learning Paraphrases from Text. Ph.D. thesis, University of Southern California, Los Angeles.Cheung, Mei Ling Lisa. 2009. Merging Corpus Linguistics and Collaborative Knowledge Construction. Ph.D. thesis, University of Birmingham, Birmingham.Cohn, T., Callison-Burch, C., & Lapata, M. (2008). Constructing Corpora for the Development and Evaluation of Paraphrase Systems. Computational Linguistics, 34(4), 597-614. doi:10.1162/coli.08-003-r1-07-044Dras, Mark. 1999. Tree Adjoining Grammar and the Reluctant Paraphrasing of Text. Ph.D. thesis, Macquarie University, Sydney.Faigley, L., & Witte, S. (1981). Analyzing Revision. College Composition and Communication, 32(4), 400. doi:10.2307/356602Fujita, Atsushi. 2005. Automatic Generation of Syntactically Well-formed and Semantically Appropriate Paraphrases. Ph.D. thesis, Nara Institute of Science and Technology, Nara.Grozea, C., & Popescu, M. (2010). Who’s the Thief? Automatic Detection of the Direction of Plagiarism. Lecture Notes in Computer Science, 700-710. doi:10.1007/978-3-642-12116-6_59GÜLICH, E. (2003). Conversational Techniques Used in Transferring Knowledge between Medical Experts and Non-experts. Discourse Studies, 5(2), 235-263. doi:10.1177/1461445603005002005Harris, Z. S. (1957). Co-Occurrence and Transformation in Linguistic Structure. Language, 33(3), 283. doi:10.2307/411155KETCHEN Jr., D. J., & SHOOK, C. L. (1996). THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE. Strategic Management Journal, 17(6), 441-458. doi:10.1002/(sici)1097-0266(199606)17:63.0.co;2-gMcCarthy, D., & Navigli, R. (2009). The English lexical substitution task. Language Resources and Evaluation, 43(2), 139-159. doi:10.1007/s10579-009-9084-1Recasens, M., & Vila, M. (2010). On Paraphrase and Coreference. Computational Linguistics, 36(4), 639-647. doi:10.1162/coli_a_00014Shimohata, Mitsuo. 2004. Acquiring Paraphrases from Corpora and Its Application to Machine Translation. Ph.D. thesis, Nara Institute of Science and Technology, Nara.Stein, B., Potthast, M., Rosso, P., Barrón-Cedeño, A., Stamatatos, E., & Koppel, M. (2011). Fourth international workshop on uncovering plagiarism, authorship, and social software misuse. ACM SIGIR Forum, 45(1), 45. doi:10.1145/1988852.198886

Predictors of Paraphrasing Website Use in Undergraduate College Students

While instances of cut and paste plagiarism among undergraduate college students has decreased over the past several years, a new form of plagiarism has emerged that often goes undetected by these software systems. Paraphrasing websites allow users to enter information from a source, which the website will then reword, giving the appearance of an original work. When used by undergraduate college students, these students are not only committing plagiarism, but they are also not learning the material or working to develop writing skills. While much research has been conducted on the quality of writing created by paraphrasing websites, little research has been devoted to understanding the factors that predict paraphrasing website use among undergraduate college students. The present study was conducted to determine if predictors of other forms of plagiarism among undergraduate college students, such as academic locus of control, academic-self-efficacy, honors status, academic year, and being a community college transfer student also predict paraphrasing website use in undergraduate college students. Undergraduate college students at a for-profit university in Connecticut completed a survey measuring these variables, and the collected data were analyzed using logistic regression. Although the results of the logistic regression indicated that academic locus of control, academic-self-efficacy, honors status, academic year, and being a community college transfer student did not predict paraphrasing website use in undergraduate college students, the results of this study still have important implications for college instructors as well as researchers who study predictors of plagiarism in undergraduate college students