Investigating the Effectiveness of Deep Learning Methods for Citation-based Plagiarism Detection PDF Download

Author: Ruiheng Wu
Publisher:
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Category :
Languages : en
Pages :

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Author: Bela Gipp
Publisher: Springer
ISBN: 3658063947
Category : Computers
Languages : en
Pages : 369

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Plagiarism is a problem with far-reaching consequences for the sciences. However, even today’s best software-based systems can only reliably identify copy & paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The author proposes Citation-based Plagiarism Detection to address this shortcoming. Unlike character-based approaches, this approach does not rely on text comparisons alone, but analyzes citation patterns within documents to form a language-independent "semantic fingerprint" for similarity assessment. The practicability of Citation-based Plagiarism Detection was proven by its capability to identify so-far non-machine detectable plagiarism in scientific publications.

Author: Man Yan Miranda Chong
Publisher:
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Category :
Languages : en
Pages :

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Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identi cation tasks. The hypothesis is that original texts and rewritten texts exhibit signi cant but measurable di erences, and that these di erences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are de ned. First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the vii traditional string-matching approaches. The objective is to investigate and evaluate the in uence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings. Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a thresholdsetting approach against a supervised machine learning classi er. Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The rst two stages are based on longer text lengths and the nal stage is based on segments of texts. Finally, the plagiarism direction identi cation problem is explored as supervised machine learning classi cation and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classi cation or ranking task is to t a piece of text into a pattern. The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and viii deep techniques helps improve the classi cation of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identi ed by statistical and linguistic traits. The conclusions of this study o er ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.

Author: Andrew Y. C. Nee
Publisher: Springer Science & Business Media
ISBN: 9814451487
Category : Technology & Engineering
Languages : en
Pages : 719

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This edited volume presents the proceedings of the 20th CIRP LCE Conference, which cover various areas in life cycle engineering such as life cycle design, end-of-life management, manufacturing processes, manufacturing systems, methods and tools for sustainability, social sustainability, supply chain management, remanufacturing, etc.

Author: D. B. Jones
Publisher: Routledge
ISBN: 1134227450
Category : Language Arts & Disciplines
Languages : en
Pages : 419

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Studies in Computational Linguistics presents authoritative texts from an international team of leading computational linguists. The books range from the senior undergraduate textbook to the research level monograph and provide a showcase for a broad range of recent developments in the field. The series should be interesting reading for researchers and students alike involved at this interface of linguistics and computing.

Author: Iana Atanassova
Publisher: Frontiers Media SA
ISBN: 2889459640
Category :
Languages : en
Pages : 134

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Author: Wendy Sutherland-Smith
Publisher: Routledge
ISBN: 1134081804
Category : Computers
Languages : en
Pages : 235

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Written for Higher Education educators, managers and policy-makers, Plagiarism, the Internet and Student Learning combines theoretical understandings with a practical model of plagiarism and aims to explain why and how plagiarism developed. It offers a new way to conceptualize plagiarism and provides a framework for professionals dealing with plagiarism in higher education. Sutherland-Smith presents a model of plagiarism, called the plagiarism continuum, which usefully informs discussion and direction of plagiarism management in most educational settings. The model was developed from a cross-disciplinary examination of plagiarism with a particular focus on understanding how educators and students perceive and respond to issues of plagiarism. The evolution of plagiarism, from its birth in Law, to a global issue, poses challenges to international educators in diverse cultural settings. The case studies included are the voices of educators and students discussing the complexity of plagiarism in policy and practice, as well as the tensions between institutional and individual responses. A review of international studies plus qualitative empirical research on plagiarism, conducted in Australia between 2004-2006, explain why it has emerged as a major issue. The book examines current teaching approaches in light of issues surrounding plagiarism, particularly Internet plagiarism. The model affords insight into ways in which teaching and learning approaches can be enhanced to cope with the ever-changing face of plagiarism. This book challenges Higher Education educators, managers and policy-makers to examine their own beliefs and practices in managing the phenomenon of plagiarism in academic writing.

Author: Debora Weber-Wulff
Publisher: Springer Science & Business
ISBN: 3642399614
Category : Computers
Languages : en
Pages : 208

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Since human beings have been writing it seems there has been plagiarism. It is not something that sprouted with the advent of the Internet. Teachers have been struggling for years in countries all over the globe to find good methods for dealing with the problem of plagiarizing students. How do we spot plagiarism? How do we teach them not to plagiarize? And how do we deal with those who have been found out to be plagiarists? The purpose of this book is to collect material on the various aspects of plagiarism in education with special attention given to the German problem of dissertation plagiarism. Since there is a wide-spread interest in the German plagiarism situation and in strategies for dealing with it, the book is written in English in order to be accessible to a larger audience.

Author: Sonja Bjelobaba
Publisher: Springer Nature
ISBN: 303116976X
Category : Education
Languages : en
Pages : 382

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This book aims to broaden the horizons of academic integrity by discussing novel practices and technologies, and the importance of student involvement in building a culture of academic integrity. Examples are the outreach efforts towards a range of non-educational organisations, the exploration and comparison of ethical policies and actions in different institutions, and the improvement of student responses in research on sensitive topics. It explores a range of scenarios and strategies adopted in different parts of the world during the COVID-19 pandemic, and addresses new technological advances for investigating types of academic misconduct that are difficult to find, including translation plagiarism, contract cheating, the usage of the proctoring systems, and the innovative use of data mining to detect cheating on on-line quizzes. The work shows how working with students is an essential part of the fight against academic misconduct. The student voice can be a powerful source of motivation for students, but educators also need to understand their perspectives, especially regarding such an important topic as academic integrity.

Author: Josep Lladós
Publisher: Springer Nature
ISBN: 3030863379
Category : Computers
Languages : en
Pages : 807

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This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: scene text detection and recognition, document classification, gold-standard benchmarks and data sets, historical document analysis, and handwriting recognition. In addition, the volume contains results of 13 scientific competitions held during ICDAR 2021.