Open AccessProceedings Article
An Evaluation Framework for Plagiarism Detection
Martin Potthast,Benno Stein,Alberto Barrón-Cedeño,Paolo Rosso +3 more
- pp 997-1005
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TLDR
Empirical evidence is given that the construction of tailored training corpora for plagiarism detection can be automated, and hence be done on a large scale.Abstract:
We present an evaluation framework for plagiarism detection. The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon's Mechanical Turk. We discuss the construction principles behind the measures and the corpus, and we compare the quality of our corpus to existing corpora. Our analysis gives empirical evidence that the construction of tailored training corpora for plagiarism detection can be automated, and hence be done on a large scale.read more
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Overview of the 2nd International Competition on Plagiarism Detection
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Improving the Reproducibility of PAN’s Shared Tasks:
TL;DR: This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling, which forms the largest collection of softwares for these tasks to date.
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References
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Hierarchical Clustering Algorithms for Document Datasets
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Proceedings ArticleDOI
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
Regina Barzilay,Lillian Lee +1 more
TL;DR: This work applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences.