Developing a corpus of plagiarised short answers

Clough, Paul; Stevenson, Mark

doi:10.1007/s10579-009-9112-1

Cited by 97 publications

(79 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting essays represent the to-date largest corpus of realistic text reuse cases available, and they have been employed to evaluate another 33 plagiarism detectors in the past three labs [43,44,45]. Besides the mentioned corpora, there are two other ones that comprise text reuse, namely the Meter corpus [8] and the Clough09 corpus [9]. The former contains 445 cases of text reuse among 1716 news articles, whereas the latter contains 57 short cases of manually generated plagiarism.…”

Section: Related Workmentioning

confidence: 99%

Improving the Reproducibility of PAN’s Shared Tasks:

Potthast

Gollub

Rangel

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Potthast, M.; Gollub, T.; Rangel, F.; Rosso, P.; Stamatatos, E.; Abstract This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling.To improve the reproducibility of shared tasks in general, and PAN's tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.

show abstract

Section: Related Workmentioning

confidence: 99%

Improving the Reproducibility of PAN’s Shared Tasks:

Potthast

Gollub

Rangel

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…PAN used automatically generated and simulated examples of plagiarism since cases of true plagiarism, where the writer has re-used text with the intent of claiming authorship, are difficult to identify and distribute. 7 http://www.dsic.upv.es/grupos/nle/fire-workshop-clitr.html 8 The name of our initiative is partially inspired by the Incredible !ndia campaign name (http://www.incredibleindia.org/). …”

Section: Corpusmentioning

confidence: 99%

“…A set of simulated plagiarised documents was created using an approach based on the one described by [8]. Participants were provided with a set of questions and asked to write a short answer, either by re-using text from a source provided (Wikipedia) or by looking at learning material (e.g.…”

Section: Generating Cases Of Plagiarismmentioning

confidence: 99%

“…The final method, Non-plagiarism, generates answers which are not plagiarised to be used for comparison. This approach was originally developed for the creation of a monolingual corpus of simulated plagiarism [8]: the sources (i.e. Wikipedia articles and learning materials) were in English and participants were asked to write answers in English.…”

Section: Generating Cases Of Plagiarismmentioning

confidence: 99%

“…However, benchmarks are needed to assist in the development and evaluation of methods for detecting cross-language text re-use. The Cross-Language !ndian Text Re-Use detection task (CL!TR) 8 at FIRE addresses this issue.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PAN@FIRE: Overview of the Cross-Language !ndian Text Re-Use Detection Competition

Barrón-Cedeño

Rosso

Devi

et al. 2013

Multilingual Information Access in South Asian Languages

Self Cite

View full text Add to dashboard Cite

Abstract. The development of models for automatic detection of text re-use and plagiarism across languages has received increasing attention in the last years. However, the lack of an evaluation framework composed of annotated datasets has caused these efforts to be isolated. In this paper we present the CL!TR 2011 corpus, the first manually created corpus for the analysis of cross-language text re-use between English and Hindi. The corpus was used during the Cross-Language !ndian Text Re-Use Detection Competition. Here we overview the approaches applied the contestants and evaluate their quality when detecting a re-used text together with its source.

show abstract

Plagiarism detection using stopword n-grams

Stamatatos

2011

J. Am. Soc. Inf. Sci.

100

View full text Add to dashboard Cite

In this paper, a novel method for detecting plagiarized passages in document collections is presented. In contrast to previous work in this field that uses content terms to represent documents, the proposed method is based on a small list of stopwords (i.e., very frequent words). We show that stopword n-grams reveal important information for plagiarism detection since they are able to capture syntactic similarities between suspicious and original documents and they can be used to detect the exact plagiarized passage boundaries. Experimental results on a publicly-available corpus demonstrate that the performance of the proposed approach is competitive when compared with the best reported results. More importantly, it achieves significantly better results when dealing with difficult plagiarism cases where the plagiarized passages are highly modified and most of the words or phrases have been replaced with synonyms.2

show abstract

Developing a corpus of plagiarised short answers

Cited by 97 publications

References 33 publications

Improving the Reproducibility of PAN’s Shared Tasks:

Improving the Reproducibility of PAN’s Shared Tasks:

PAN@FIRE: Overview of the Cross-Language !ndian Text Re-Use Detection Competition

Plagiarism detection using stopword n-grams

Contact Info

Product

Resources

About