Thispaper overviews 18 plagiarism detectors that have been developed and evaluated within PAN'10. We start with a unified retrieval process that sum- marizes the best practices employed this year. Then, the detectors' performances are evaluated in detail, highlighting several important aspects of plagiarism de- tection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year's competition.

/pdf/overview-of-the-2nd-international-competition-on-plagiarism-30hy5ipx0c.pdf

Overview of the 2nd International Competition on Plagiarism Detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.

The presented experiments show that i more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, ii lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and iii paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.

/pdf/plagiarism-meets-paraphrasing-insights-for-the-next-51nfph38tg.pdf

Plagiarism meets paraphrasing: Insights for the next generation in automatic plagiarism detection

Plagiarism detection is of special interest to educational institutions, and with the proliferation of digital documents on the Web the use of computational systems for such a task has become important. While traditional methods for automatic detection of plagiarism compute the similarity measures on a document-to-document basis, this is not always possible since the potential source documents are not always available. We do text mining, exploring the use of words as a linguistic feature for analyzing a document by modeling the writing style present in it. The main goal is to discover deviations in the style, looking for segments of the document that could have been written by another person. This can be considered as a classification problem using self-based information where paragraphs with significant deviations in style are treated as outliers. This so-called intrinsic plagiarism detection approach does not need comparison against possible sources at all, and our model relies only on the use of words, so it is not language specific. We demonstrate that this feature shows promise in this area, achieving reasonable results compared to benchmark models.

https://www.dii.uchile.cl/wp-content/uploads/2014/06/Text-mining-applied-to-plagiarism-detection.pdf

Text mining applied to plagiarism detection: The use of words for detecting deviations in the writing style

Plagiarism is described as the reuse of someone else's previous ideas, work or even words without sufficient attribution to the source. This paper presents a method to detect external plagiarism using the integration of semantic relations between words and their syntactic composition. The problem with the available methods is that they fail to capture the meaning in comparison between a source document sentence and a suspicious document sentence, when two sentences have same surface text (the words are the same) or they are a paraphrase of each other. Therefore it causes inaccurate or unnecessary matching results. However, this method can improve the performance of plagiarism detection because it is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. It is executed by computing the semantic and syntactic similarity of the sentence-to-sentence. Besides, the proposed method expands the words in sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed in a different wording. This method is also capable to identify various kinds of plagiarism such as the exact copied text, paraphrasing, transformation of sentences and changing of word structure in the sentences. 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. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-10 and PAN-PC-11 datasets.

PDLK: Plagiarism detection using linguistic knowledge

The proposed work aims to explore and compare the potency of syntactic-semantic based linguistic structures in plagiarism detection using natural language processing techniques. The current work explores linguistic features, viz., part of speech tags, chunks and semantic roles in detecting plagiarized fragments and utilizes a combined syntactic-semantic similarity metric, which extracts the semantic concepts from WordNet lexical database. The linguistic information is utilized for effective pre-processing and for availing semantically relevant comparisons. Another major contribution is the analysis of the proposed approach on plagiarism cases of various complexity levels. The impact of plagiarism types and complexity levels, upon the features extracted is analyzed and discussed. Further, unlike the existing systems, which were evaluated on some limited data sets, the proposed approach is evaluated on a larger scale using the plagiarism corpus provided by PAN11http://pan.webis.de. competition from 2009 to 2014. The approach presented considerable improvement in comparison with the top-ranked systems of the respective years. The evaluation and analysis with various cases of plagiarism also reflected the supremacy of deeper linguistic features for identifying manually plagiarized data.

Unmasking text plagiarism using syntactic-semantic based natural language processing techniques

This paper aims to explain the performance of plagiarism detection system which can detect External as well as Intrinsic Plagia- rism in text. It reports the results on PAN-PC-2011 test corpus. We investigated Vector Space Model based techniques for detecting external plagiarism cases and discourse markers based features to detect intrinsic plagiarism cases.

http://ceur-ws.org/Vol-1177/CLEF2011wn-PAN-RaoEt2011.pdf

External & Intrinsic Plagiarism Detection : VSM & Discourse Markers based Approach Notebook for PAN at CLEF 2011

We tried Named Entity features of source documents to identify its suspicious counter part. A three stage identification method was adopted to understand the impact of NEs in plagiarism. Results along with a brief analysis are given in this note.

/pdf/external-plagiarism-detection-n-gram-approach-using-named-3076ur0n6z.pdf

Sameer Rao

Papers

External & Intrinsic Plagiarism Detection : VSM & Discourse Markers based Approach Notebook for PAN at CLEF 2011

External Plagiarism Detection: N-Gram Approach Using Named Entity Recognizer - Lab Report for PAN at CLEF 2010.