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Reuse and plagiarism in Speech and Natural Language Processing publications

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Abstract

The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy and paste operations between articles in the domain of Natural Language Processing (NLP), including Speech Processing. The search space of the comparisons is a corpus labeled as NLP4NLP, which includes 34 different conferences and journals and gathers a large part of the NLP activity over the past 50 years. This study considers the similarity between the papers of each individual event and the complete set of papers in the whole corpus, according to four different types of relationship (self-reuse, self-plagiarism, reuse and plagiarism) and in both directions: a paper borrowing a fragment of text from another paper of the corpus (that we will call the source paper), or in the reverse direction, fragments of text from the source paper being borrowed and inserted in another paper of the corpus. The results show that self-reuse is rather a common practice, but that plagiarism seems to be very unusual, and that both stay within legal and ethical limits.

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Notes

  1. http://pan.webis.de.

  2. www.nlp4nlp.org.

  3. The total number of papers is 67,937, but in the case of a joint conference, the papers are counted twice. This number reduces to 65,003, if we count duplicate papers only once. Similarly, the number of venues is 577 when all venues are counted, but this number reduces to 558 when the 19 joint conferences are counted only once.

  4. http://aclweb.org/anthology.

  5. www.isca-speech.org/iscaweb/index.php/archive/online-archive.

  6. www.ieee.org/index.html.

  7. http://pdfbox.apache.org.

  8. www.bouncycastle.org/.

  9. http://code.google.com/p/tesseract-ocr.

  10. TagParser is a tool created and distributed by Tagmatica (see www.tagmatica.com).

  11. http://github.com/knmnyn/ParsCit.

  12. http://github.com/kermitt2/grobid.

  13. Also called “n-grams” in some NLP papers.

  14. Concerning this specific problem, for instance, PACLIC and COLING which are one column formatted give much better extraction quality than LREC and ACL which are two columns formatted.

  15. It takes 69 h instead of 44 h on a mid-range mono-processor Xeon E3-1270 V2 with 32 GB of RAM.

  16. But the space limitations do not allow to present these results in lengthy details. Furthermore, we do not want to display personal results.

  17. http://en.wikipedia.org/wiki/Right_to_quote.

  18. http://en.wikipedia.org/wiki/Rogeting.

  19. To this regard, the reader will find a certain degree of overlapping between this paper and the one we published at LREC 2016 on reuse and plagiarism limited to the LREC papers, regarding the description of the NLP4NLP corpus and of the similarity measure algorithm.

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Authors and Affiliations

  1. LIMSI, CNRS, Université Paris-Saclay, Orsay, France

    Joseph Mariani, Gil Francopoulo & Patrick Paroubek

  2. Tagmatica, Paris, France

    Gil Francopoulo

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  1. Joseph Mariani
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  2. Gil Francopoulo
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Correspondence to Joseph Mariani.

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Mariani, J., Francopoulo, G. & Paroubek, P. Reuse and plagiarism in Speech and Natural Language Processing publications. Int J Digit Libr 19, 113–126 (2018). https://doi.org/10.1007/s00799-017-0211-0

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