Reuse and plagiarism in Speech and Natural Language Processing publications

Abstract

The goal of this paper is to propose measures of innovation through the study of publications in the field of speech and language processing. It is based on the NLP4NLP corpus, which contains the articles published in major conferences and journals related to speech and language processing over 50 years . It represents 65,003 documents from 34 different sources, conferences and journals, published by 48,894 different authors in 558 events, for a total of more than 270 million words and 324,422 bibliographical references. The data was obtained in textual form or as an image that had to be converted into text. This resulted in a lower quality for the most ancient papers, that we measured through the computation of an unknown word ratio. The multi-word technical terms were automatically extracted after parsing, using a set of general language text corpora. The occurrences, frequencies, existences and presences of the terms were then computed overall, for each year and for each document. It resulted in a list of 3.5 million different terms and 24 million term occurrences. The evolution of the research topics over the year, as reflected by the terms presence, was then computed and we propose a measure of the topic popularity based on this computation. The author(s) who introduced the terms were searched for, together with the year when the term was first introduced and the publication where it was introduced. We then studied the global and evolutional contributions of authors to a given topic. We also studied the global and evolutional contributions of the various publications to a given topic. We finally propose a measure of innovativeness for authors and publications.

The NLP4NLP Corpus (II): 50 Years of Research in Speech and Language Processing

“…Here we study the reuse of NLP4NLP papers in other NLP4NLP papers (Mariani et al, 2016(Mariani et al, , 2017a.…”

“…We then studied when and who introduced new terms, as a mark of the innovative ability of various authors, which may also provide an estimate of their contribution to the advances of the scientific domain (Mariani et al, 2018a). We make the hypothesis that an innovation is induced by the introduction of a term which was previously unused in the community and then became popular.…”

Introduction to the special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL)

“…2 This special issue resulting from the BIRNDL workshop includes 14 papers: four extended papers presented at the first BIRNDL workshop and the BIR workshop at ECIR 2016 [2,8,14,18], three extended system reports of the CL-SciSumm Shared Task 2016 [1,13,16] and one overview paper [11] and six original research papers submitted via the open call for papers [6,7,9,10,12,17]. guage Processing publications" [14] to detect extrinsic instances of self-reuse, self-plagiarism, reuse and plagiarism in NLP and speech processing articles. By comparing word sequences in publications from the NLP4NLP corpus, consisting of more than 65,000 documents, the authors found that self-reuse (i.e., the reuse of text from another document on which there is a common author and that is cited) is relatively common, but reuse of content from others papers that have been cited and plagiarism, where there is no attribution, were quite rare and remained within ethical limits.…”

Study on Extrinsic Text Plagiarism Detection Techniques and Tools

“…Alzahrani et al, [53] utilized citation evidences along with structural detection for detecting plagiarism cases. Four types of plagiarism, viz., self-reuse, self-plagiarism, reuse and plagiarism is detected using text based detection with citation analysis to detect copy-paste plagiarism in scientific articles of NLP domain by Mariani et al [57]. They used papers from different websites such as ACL Anthology, ISCA archive and IEEE in NLP and speech processing.…”