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Plagiarism detection

About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.


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Journal ArticleDOI
TL;DR: The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism.
Abstract: Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS) tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7%) handmade paraphrases, 54,815 (55.4%) artificial plagiarism cases, and 32,578 (32.9%) plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA) statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.

20 citations

Journal ArticleDOI
TL;DR: An External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information, and the content word expansion approach to detect different types of plagiarism.
Abstract: Plagiarism is the unauthorized use of the ideas, presentation of someone else's words or work as your own. This paper presents an External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information. Most of the available methods fail to capture the meaning in the comparison between a source document sentence and a suspicious document sentence when two sentences have same surface text. Therefore, it leads to incorrect or even unnecessary matching results. However, the proposed method is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. On the other hand, an author may change the sentence from: active to passive and vice versa; hence, the method also employed the SRL technique to tackle the aforementioned challenge. Furthermore, the method used the content word expansion approach to bridge the lexical gaps and identify the similar ideas that are expressed using different wording. The proposed method is able to detect different types of plagiarism such as the exact verbatim copying, paraphrasing, transformation of sentences, changing of word structure. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11 and other existing techniques.

20 citations

01 Jan 2016
TL;DR: A plagiarism detection method based on constructing an author style function from features of text sentences and detecting outliers and adapted the method for the diarization problem by segmenting author style statistics on text parts, which correspond to different authors.
Abstract: The paper investigates methods for intrinsic plagiarism detection and author diarization. We developed a plagiarism detection method based on constructing an author style function from features of text sentences and detecting outliers. We adapted the method for the diarization problem by segmenting author style statistics on text parts, which correspond to different authors. Both methods were tested on the PAN-2011 collection for the intrinsic plagiarism detection and implemented for the PAN-2016 competition on author diarization.

20 citations

Proceedings ArticleDOI
25 Jul 2001
TL;DR: VAST improves on the human-eye approach by identifying similarities which a tutor might otherwise miss, thus saving investigative time, and is demonstrated using noise-free synthetic texts and actual student submissions containing intra-corpal plagiarism.
Abstract: Describes the VAST (Visualisation and Analysis of Similarity Tool) prototype system, which tutors can use to investigate student submissions for intra-corpal plagiarism. VAST displays a pair of student submissions and a graphical representation of their similarity, allowing tutors to navigate directly to areas of potential plagiarism. It improves on the human-eye approach by identifying similarities which a tutor might otherwise miss, thus saving investigative time. VAST is demonstrated using noise-free synthetic texts and actual student submissions containing intra-corpal plagiarism. Associated ideas, including similarity visualisations, similarity intersections and the "four-stage plagiarism detection process" are also introduced.

20 citations

20 Sep 2012
TL;DR: This paper discusses the approaches to these tasks in the Author Identification track at PAN2012, which represents the first proper attempt at any of them, and discusses the results achieved using some simple but relatively novel approaches.
Abstract: Tasks such as Authorship Attribution, Intrinsic Plagiarism detection and Sexual Predator Identification are representative of attempts to deceive. In the first two, authors try to convince others that the presented work is theirs, and in the third there is an attempt to convince readers to take actions based on false beliefs or ill-perceived risks. In this paper, we discuss our approaches to these tasks in the Author Identification track at PAN2012, which represents our first proper attempt at any of them. Our initial intention was to determine whether cues of deception, documented in the literature, might be relevant to such tasks. However, it quickly became apparent that such cues would not be readily useful, and we discuss the results achieved using some simple but relatively novel approaches: for the Traditional Authorship Attribution task, we show how a mean-variance framework using just 10 stopwords detects 42.8% and could be obtain 52.12% using fewer; for Intrinsic Plagiarism Detection, frequent words achieved 91.1% overall; and for Sexual Predator Identification, we used just a few features covering requests for personal information, with mixed results.

20 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202359
2022126
202183
2020118
2019130
2018125