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Scholarly literature mining with information retrieval and natural language processing: Preface

  • Guillaume Cabanac
    ,
  • Ingo Frommholz
    ,
  • Philipp Mayr
  • University of Toulouse III
    ,
  • Leibniz Institute for the Social Sciences
Research Output: Contribution to journal Editorial

Abstract

This special issue features the work of authors originally coming from different communities: bibliometrics/scientometrics (SCIM), information retrieval (IR) and, as an emerging player gaining more relevance for both aforementioned fields, natural language processing (NLP). The work presented in their papers combine ideas from all these fields, having in common that they all are using the scholarly data well known in scientometrics and solving problems typical to scientometric research. They model and mine citations, as well as metadata of bibliographic records (authorships, titles, abstracts sometimes), which is common practice in SCIM. They also mine and process fulltexts (including in-text references and equations) which is common practice in IR and requires established NLP text mining techniques. IR collections are utilised to ensure reproducible evaluations; creating and sharing test collections in evaluation initiatives such as CLEF eHealth is common IR tradition that is also prominent in NLP, eg., by the CL-SciSumm shared task.

Publication Information

Output type

Research Output: Contribution to journal Editorial

Original language

English

Pages from-to (Number of pages)

Pages 2835-2840 (6 pages)

Journal (Volume, Issue Number)

Scientometrics (Volume 125, Issue 3)

Publication milestones

  • Published - 17/11/2020

Publication status

Published - 17/11/2020

ISSN

0138-9130

External Publication IDs

  • Scopus: 85096147342

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