Plagiarism Detection through Multilevel Text Comparison

Zini, Matteo; Fabbri, Marco; Moneglia, Massimo; Panunzi, Alessandro

doi:10.1109/axmedis.2006.40

Cited by 23 publications

(10 citation statements)

References 4 publications

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“…Plagiarism detection, like many research-paper recommenders, uses text and citation analysis to identify similar documents [252][253][254]. Additionally, research relating to crawling the web and analyzing academic ar6cles can be useful for building research-paper recommender systems, for instance, author 1 4 [255], title extraction [256][257][258][259][260], or citation extraction and matching [261].…”

Section: R Elated Research Fieldsmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: R Elated Research Fieldsmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Many researchers use data mining methods to improve current software which detects plagiarism. Some authors, while working with text mining methods, present a new architecture for a plagiarism detection tool that can work with many different kinds of digital submissions, from plain or formatted texts to audio podcasts (Butakov and Scherbinin 2008;Zini et al 2006). They have developed anti-plagiarism toolbox, which search plagiarism in local and in global databases (internet by using Microsoft Live Search).…”

Section: Related Workmentioning

confidence: 99%

Analysis of Students’ Study Activities in Virtual Learning Environments Using Data Mining Methods / Studentų, Besimokančių Virtualaus Mokymo Aplinkoje, Veiklos Analizė Taikant Duomenų Gavybos Metodus

Preidys¹,

Sakalauskas²

2010

Technological and Economic Development of Economy

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Abstract. This article deals with application of data mining methods' to analysis of learners' behaviour using the distance learning platform BlackBoard Vista (BlackBoard 2008). Before planning a distance learning course, instructors have to pay attention to the fact that there exist different study methods: some students start reading learning materials from the very beginning to the end, some students look at unclear topics only, some start with the discussions, etc. Therefore after analyzing the learning factors and identifying learner's style, it is possible to prepare individualized learning materials and to choose a proper way of course presentation. Such a way of study organization would improve the quality of studies and make it possible to reach better results. The research was performed by observing the behaviour and results achieved by 528 students in 15 distance learning courses and, using the clustering method, 3 learner's styles using virtual learning environments (VLE) have been identified and work methods proposed for students with regard to those learners' styles. Besides, the research aims to find out the factors that influence final evaluations of students' .

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“…KNN search on sequences have applications in a variety of areas including DNA/protein sequence search [13], approximate keyword search [1], and plagiarism detection [17,30].…”

Section: Introductionmentioning

confidence: 99%

Efficient and effective KNN sequence search with approximate n-grams

et al. 2013

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In this paper, we address the problem of finding k-nearest neighbors (KNN) in sequence databases using the edit distance. Unlike most existing works using short and exact ngram matchings together with a filter-and-refine framework for KNN sequence search, our new approach allows us to use longer but approximate n-gram matchings as a basis of KN-N candidates pruning. Based on this new idea, we devise a pipeline framework over a two-level index for searching KNN in the sequence database. By coupling this framework together with several efficient filtering strategies, i.e. the frequency queue and the well-known Combined Algorithm (CA), our proposal brings various enticing advantages over existing works, including 1) huge reduction on false positive candidates to avoid large overheads on candidate verifications; 2) progressive result update and early termination; and 3) good extensibility to parallel computation. We conduct extensive experiments on three real datasets to verify the superiority of the proposed framework.

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Plagiarism Detection through Multilevel Text Comparison

Cited by 23 publications

References 4 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Analysis of Students’ Study Activities in Virtual Learning Environments Using Data Mining Methods / Studentų, Besimokančių Virtualaus Mokymo Aplinkoje, Veiklos Analizė Taikant Duomenų Gavybos Metodus

Efficient and effective KNN sequence search with approximate n-grams

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