An Evaluation Framework for Plagiarism Detection
By Martin Potthast, Benno Stein, Alberto Barrón-Cedeño, Paolo Rosso. International Conference on Computational Linguistics, 2010
362 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/b193f6e669c49f45c61d7d9dec034303d52d070e
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Citations in the corpus
- Re-examining Machine Translation Metrics for Paraphrase Identification (2012, methodology): “…for identifying paraphrases across two different datasets - the well-known Microsoft Research paraphrase corpus (MSRP) (Dolan et al., 2004) and the plagiarism detection corpus (PAN) from the 2010 Uncovering Plagiarism, Authorship and Social Software Misuse shared task (Potthast et al., 2010).”
- SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020, methodology): “We compute precision P and recall R by adapting the formulas in (Potthast et al., 2010): We define Eq.”
- Fine-Grained Analysis of Propaganda in News Article (2019, background): “6 We derive an ad hoc measure following related work on named entity recognition (NER) (Nadeau and Sekine, 2007) and (intrinsic) plagiarism detection (PD) (Potthast et al., 2010).”
- Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy (2022, background): “Existing plagiarism tools, and their evaluations, go far beyond verbatim matches and consider fuzzy data “clones” ranging from simple transformations (e.g., word variations or shuffles) to arbitrary semantics-preserving para-phrasing (Roy et al., 2009; Potthast et al., 2010).”
- An Empirical Analysis of NMT-Derived Interlingual Embeddings and Their Use in Parallel Sentence Identification (2017, background): “sentially similarity assessment and so is the task of plagiarism detection [15].”
- Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection (2021, background): “phrases and substituting words with their synonyms, antonyms, hyponyms, or hypernyms selected at random [12].”