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Filtering by: Creator Scharpf, Philipp Remove constraint Creator: Scharpf, Philipp Keyword paraphrase detection Remove constraint Keyword: paraphrase detection Resource type Dataset Remove constraint Resource type: Dataset

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  • Detecting Machine-obfuscated Plagiarism

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    Creator:
    Foltynek, Tomas, Ruas, Terry, Scharpf, Philipp , Meuschke, Norman, Schubotz, Moritz , Grosky, William , and Gipp, Bela
    Description:
    This data set is comprised of multiple folders. The corpus folder contains raw text used for training and testing in two splits, "document" and "paragraph". The Spun documents and paragraphs are generated using the SpinBot tool ( https://spinbot.com/API). The paragraph split is generated by only selecting paragraphs with 3 or more sentences in the document split. Each folder is divided in mg (i.e., machine generated through SpinBot) and og (i.e., original generated file), The human judgement folder contains the human evaluation between original and spun documents (sample). It also contains the answers (keys) and survey results. , The models folder contains the machine learning classifier models for each word embedding technique used (only for document split training). The models were exported using pickle (Python 3.6). The grid search for hyperparameter adjustments is described in the paper. , and The vector folders (train and test) contains the average of all word vectors for each document and paragraph. Each line has the number of dimensions of the word embeddings technique used (see paper for more details) followed by its respective class (i.e, label mg or og). Each file belong to one class, either "mg" or "og". The values are comma-separated (.csv). The extension is .arff can be read as a normal .txt file.
    Keyword:
    paraphrase detection, plagiarism detection, document classification, and word embeddings
    Citation to related publication:
    Foltýnek, T. & Ruas, T. & Scharpf, P. & Meuschke, N. & Schubotz, M. & Grosky, W. & Gipp, B., “Detecting Machine-obfuscated Plagiarism,” in Sustainable Digital Communities, vol. 12051 LNCS, Springer, 2020, pp. 816–827.  https://doi.org/10.1007/978-3-030-43687-2_68
    Discipline:
    General Information Sources