Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words.

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Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

In this paper we describe a cluster-based plagiarism detection method, which we have used in the learning management system of SCUT to detect plagiarism in the network engineering related courses And we also used this method to detect external plagiarism in the PAN-10 competition The method is divided into three steps: the first step, called pre-selecting, is to narrow the scope of detection using the successive same fingerprint; the second step, called locating, is to find and merge all fragments between two documents using cluster method; the third step, called post-processing, is to deal with some merging errors Our method ran 19 hours in the PAN-10 competition, and the result ranked the second place, which met our expectation Keywords Plagiarism detection, Similar text, Locating, Cluster

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A Cluster-Based Plagiarism Detection Method - Lab Report for PAN at CLEF 2010.

Plagiarism has become one area of interest for researchers due to its importance, and its fast growing rates. In this paper we are going to survey and list the advantage sand disadvantages of the latest and the important effective methods used or developed in automatic plagiarism detection, according to their result. Mainly methods used in natural language text detection, index structure, and external plagiarism detection and clustering -- based detection.

Survey of Plagiarism Detection Methods

Plagiarism is known as illegal use of others' part of work or whole work as one's own in any field such as art, poetry, literature, cinema, research and other creative forms of study. Plagiarism is one of the important issues in academic and research fields and giving more concern in academic systems. The situation is even worse with the availability of ample resources on the web. This paper focuses on an effective plagiarism detection tool on identifying suitable intra-corpal plagiarism detection for text based assignments by comparing unigram, bigram, trigram of vector space model with cosine similarity measure. Manually evaluated, labelled dataset was tested using unigram, bigram and trigram vector. Even though trigram vector consumes comparatively more time, it shows better results with the labelled data. In addition, the selected trigram vector space model with cosine similarity measure is compared with tri-gram sequence matching technique with Jaccard measure. In the results, cosine similarity score shows slightly higher values than the other. Because, it focuses on giving more weight for terms that do not frequently exist in the dataset and cosine similarity measure using trigram technique is more preferable than the other. Therefore, we present our new tool and it could be used as an effective tool to evaluate text based electronic assignments and minimize the plagiarism among students.

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Plagiarism detection on electronic text based assignments using vector space model

Text reuse is the process of creating new documents using existing ones. Among the different types of text reuse, plagiarism (the unacknowledged reuse of text) is a widespread problem. Easy access to online information has made it easier to plagiarise and in recent years cases of plagiarism have increased. Consequently, plagiarism and its detection is receiving attention within the research community. The main goal of this research is to develop algorithms for detecting monolingual text reuse, with a particular emphasis on mono-lingual extrinsic plagiarism detection. In this type of reuse both the source and reused texts are in the same language and the aim is to identify the source document that has been reused. Special attention is given to cases of text reuse created by paraphrasing the source document because detecting them is an open challenge. This thesis focuses on two connected problems related to the detection of mono-lingual text reuse. The first is candidate document selection, the process of comparing a document against a collection to identify a small set of “candidate documents” which include the source document(s). The second problem is pairwise document comparison, the process of comparing a pair of documents to determine whether one has reused the other. An IR-based framework is proposed for candidate document selection. To deal with cases of text reuse in which the source has been paraphrased, query expansion is incorporated into the IR-based framework. Different lexical resources for query expansion are explored. Evaluation is carried out using a variety of benchmark corpora and a new evaluation corpus created. Results showed that the proposed IR-based approach outperforms a state-of-the-art approach and is more robust in detecting verbatim (word to word copy) and modified copies of texts. Query expansion is found to be useful in detecting cases of reuse when the source document has been heavily paraphrased. A system is also developed to carry out a pairwise comparison of documents. To deal with cases when the source text has been paraphrased, an n-gram overlap approach is extended with modified n-grams created by substituting words with synonyms and deleting words in an n-gram. Various lexical resources are used for substituting words with synonyms. A range of benchmark corpora are used for evaluation. Results showed that the modified n-gram approach improves performance when the source text has been paraphrased to create the reused text. The thesis explores the problems of candidate document retrieval and pairwise document comparison for mono-lingual text reuse detection. It shows how techniques can be developed for these problems and that they can be extended to identify cases of text reuse when the source document has been paraphrased. The following publications resulted from the research carried out for this thesis: [Nawab et al., 2010], [Nawab et al., 2011], [Nawab et al., 2012b] and [Nawab et al., 2012a].

Mono-lingual Paraphrased Text Reuse and Plagiarism Detection

Aiming at the Chinese academic paper plagiarism detection, proposed chunk based plagiarism detection algorithm with chunk extraction method based on character or word. Taken account of that different part of document has different importance, proposed two paragraph weight algorithms and defined three paragraph weight functions. The best chunk lengths are determined by experiments. Experiments show that using paragraph weight can enhance the detection effect.

Tao Wang

Papers

Plagiarism detection in Chinese based on chunk and paragraph weight