Editorial: Mining Scientific Papers: NLP-enhanced Bibliometrics

The Research Topic on “NLP-enhanced Bibliometrics” aims to promote interdisciplinary research in bibliometrics, Natural Language Processing (NLP) and computational linguistics in order to enhance the ways bibliometrics can benefit from large-scale text analytics and sense mining of papers. The objectives of such research are to provide insights into scientific writing and bring new perspectives to the understanding of both the nature of citations and the nature of scientific papers and their internal structures. The possibility to enrichmetadata by the full-text processing of papers offers a new field of investigation, where the major problems arise around the organization and structure of text, the extraction of information and its representation at the level of metadata. Recently, the ever growing availability of datasets and papers in full text and inmachine-readable formats has made possible a change in perspective in the field of bibliometrics. From preprint databases to the Open Access and the Open Science movements, the development of online platforms such as ArXiv, CiteSeer or PLoS and so forth, largely contribute to facilitating the experimentation with datasets of articles, making it possible to perform bibliometric studies not only considering the metadata of papers but also their full text content. The field of NLP offers methodological frameworks and tools for the full text processing of papers that can enlighten bibliometric studies. Some of the open source tools for text processing that have been recently applied to such tasks include NLTK, Mallet, OpenNLP, CoreNLP, Gate, CiteSpace, AllenNLP, and others. Many datasets are now freely available for the community: e.g., PubMed OA, CiteSeerX, JSTOR, ISTEX, Microsoft Academic Graph, ACL anthology, etc. The further developments in this field of study need producing annotated corpora and shared evaluation protocols in order to enable the comparison between different tools andmethods. The development of such resources is an important step to making scientific reproducibility possible.