A Machine Learning Approach for Plagiarism Detection

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists

Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data

Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD. The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving. The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)

Revisiting the challenges and surveys in text similarity matching and detection methods

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions

A computational academic integrity framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d¿aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

A Computational Academic Integrity Framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d'aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism

Barrón Cedeño, LA. (2012). On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16012Palanci

Using Natural Language Parsers for Authorship Attribution

The goal of authorship attribution is to find a set of unconscious writing characteristics or style features that distinguish text written by one person from text written by another. Once these features are found, they can be used to pair a text with the individual who wrote it. It is now well accepted that authors develop distinct and unconscious writing features. Over one thousand stylometric features (style markers) have been proposed in a variety of research disciplines [44] but none of that research has looked at the syntactic structure of the text. I conjectures that the distinct writing features of an author are not limited to these features already studied, but also include syntactic features. To support this hypothesis, I ran experiments using two open source parsing programs and analyzed the results to see if features given to me from these programs were enough for me to determine who is the most probable author of a text. Parsing programs are designed to determine syntactic structures in nat ural language. They take a text or a writing sample and produce output showing the grammatical relationship between the words in the text. They provide a means to test the hypothesis that authors\u27 syntactic use of words provide enough identifying characteristics to differentiate between them. Using two open source natural language parsing programs, the Link Gram mar Parser and Collins\u27 Parser, this research tested to see if an authors sentence structure is unique enough to provide a means of recognizing the probable author of a text. Initial data was collected on a pool of test au thors. Sample texts by each author were run through both parsers. The output of each parser was analyzed using two multivariate analysis methods: discriminant analysis and cluster k- means. My results show that syntactic sentence structures may be a viable method for authorship attribution. The Link Grammar shows promise as a way to augment authorship attribution methods already out there. Collins\u27 Parser provided even better results that should be solid enough to stand on their own as a new and viable alternative to methods that already exist. Collins\u27 parser also provided new predictors that might improve current authorship attribution methods. For example, elements and phrases with wh- words and the length of noun phrases are highly corrolated with authorship in this study

A study on plagiarism detection and plagiarism direction identification using natural language processing techniques

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Plagiarism detection for Indonesian texts

As plagiarism becomes an increasing concern for Indonesian universities and research centers, the need of using automatic plagiarism checker is becoming more real. However, researches on Plagiarism Detection Systems (PDS) in Indonesian documents have not been well developed, since most of them deal with detecting duplicate or near-duplicate documents, have not addressed the problem of retrieving source documents, or show tendency to measure document similarity globally. Therefore, systems resulted from these researches are incapable of referring to exact locations of ``similar passage'' pairs. Besides, there has been no public and standard corpora available to evaluate PDS in Indonesian texts. To address the weaknesses of former researches, this thesis develops a plagiarism detection system which executes various methods of plagiarism detection stages in a workflow system. In retrieval stage, a novel document feature coined as phraseword is introduced and executed along with word unigram and character n-grams to address the problem of retrieving source documents, whose contents are copied partially or obfuscated in a suspicious document. The detection stage, which exploits a two-step paragraph-based comparison, is aimed to address the problems of detecting and locating source-obfuscated passage pairs. The seeds for matching source-obfuscated passage pairs are based on locally-weighted significant terms to capture paraphrased and summarized passages. In addition to this system, an evaluation corpus was created through simulation by human writers, and by algorithmic random generation. Using this corpus, the performance evaluation of the proposed methods was performed in three scenarios. On the first scenario which evaluated source retrieval performance, some methods using phraseword and token features were able to achieve the optimum recall rate 1. On the second scenario which evaluated detection performance, our system was compared to Alvi's algorithm and evaluated in 4 levels of measures: character, passage, document, and cases. The experiment results showed that methods resulted from using token as seeds have higher scores than Alvi's algorithm in all 4 levels of measures both in artificial and simulated plagiarism cases. In case detection, our systems outperform Alvi's algorithm in recognizing copied, shaked, and paraphrased passages. However, Alvi's recognition rate on summarized passage is insignificantly higher than our system. The same tendency of experiment results were demonstrated on the third experiment scenario, only the precision rates of Alvi's algorithm in character and paragraph levels are higher than our system. The higher Plagdet scores produced by some methods in our system than Alvi's scores show that this study has fulfilled its objective in implementing a competitive state-of-the-art algorithm for detecting plagiarism in Indonesian texts. Being run at our test document corpus, Alvi's highest scores of recall, precision, Plagdet, and detection rate on no-plagiarism cases correspond to its scores when it was tested on PAN'14 corpus. Thus, this study has contributed in creating a standard evaluation corpus for assessing PDS for Indonesian documents. Besides, this study contributes in a source retrieval algorithm which introduces phrasewords as document features, and a paragraph-based text alignment algorithm which relies on two different strategies. One of them is to apply local-word weighting used in text summarization field to select seeds for both discriminating paragraph pair candidates and matching process. The proposed detection algorithm results in almost no multiple detection. This contributes to the strength of this algorithm