Editorial for the Second Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics (CLBib2017)

The Open Access movement in scientific publishing and search engines like Google Scholar have made scientific articles more broadly accessible. During the last decade, the availability of scientific papers in full text has become more and more widespread thanks to the growing number of publications on online platforms such as ArXiv, CiteSeer and Public Library of Science (PLOS). In this context, new needs arise around the processing and efficient exploitation of scientific corpora. Scientific papers are highly structured texts and display specific properties related to their references but also argumentative and rhetorical structure. Recent research in this field has concentrated on the construction of ontologies for citations and scientific articles (e.g. FaBiO and CiTO [8]) and studies of the distribution of references (see [2]). However, up to now full-text mining efforts are rarely used to provide data for bibliometric analyses. While bibliometrics traditionally relies on the analysis of metadata of scientific papers (see e.g. a recent special issue on ”Combining Bibliometrics and Information Retrieval”, Mayr & Scharnhorst [6]), we will explore the ways full-text processing of scientific papers and linguistic analyses can play. The CLBib workshop series provides a forum to discuss novel approaches and insights into scientific writing that can bring new perspectives to understand both the nature of citations and the nature of scientific articles. The possibility to enrich metadata by the full-text processing of papers offers new fields of application to bibliometrics studies.