CLBib 2015 - 2015 Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics

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 and CiteSeer.
The efforts to provide articles in machine-readable formats and the rise of Open Access publishing have resulted in a number of standardized formats for scientific papers (such as NLM-JATS, TEI, DocBook), full-text datasets for research experiments (PubMed, JSTOR, etc.) and corpora (iSearch, etc.). At the same time, research in the field of Natural Language Processing have provided a number of open source tools for versatile text processing (e.g. NLTK, Mallet, OpenNLP, CoreNLP, Gate, CiteSpace).
Motivation
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. CiTO, LinkedScience1) and studies of the distribution of references . 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, 2015), we will explore the ways full-text processing of scientific papers and linguistic analyses can play. With this workshop we like to discuss novel approaches and provide 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.
Working with full text allows us to go beyond metadata used in bibliometrics. Full text 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 on the level of metadata. Furthermore, the study of contexts around in-text citations offers new perspectives related to the semantic dimension of citations. The analyses of citation contexts and the semantic categorization of publications will allow us to rethink co-citation networks, bibliographic coupling and other bibliometric techniques.
Description
The workshop aims to bring together researchers in bibliometrics and computational linguistics in order to study the ways bibliometrics can benefit from large-scale text analytics and sense mining of scientific papers, thus exploring the interdisciplinarity of Bibliometrics and Natural Language Processing. How can we enhance author network analysis and bibliometrics using data obtained by text analytics? What insights can NLP provide on the structure of scientific writing, on citation networks, and on in-text citation analysis?
Workshop topics
Linguistic modeling and discourse analysis for scientific texts
User interfaces, text representations and visualizations
Structure of scientific articles (discourse / argumentative / rhetorical / social)
Scientific corpora and paper standards
Act of citations, in-text citations and Content Citation Analysis
Co-citation and bibliographic coupling
Text enhanced bibliographic coupling
Terminology extraction
Text mining and information extraction
Scientific information retrieval
Ontological descriptions of scientific content
Knowledge extraction
The workshop will involve research project reports, system demonstrations and a panel discussion on the perspectives for the development of new text analytics approaches for bibliometrics.