Text Mining with the Stanford CoreNLP

  • Min Song
  • Tamy Chambers


Text mining techniques have been widely employed to analyze various texts from massive social media to scientific publications and patents. As a bibliographic analysis tool the technique presents the opportunity for large-scale topical analysis of papers covering an entire domain, country, institution, or specific journal. For this project, we have chosen to use the Stanford CoreNLP parser due to its extensibility and enriched functionalities which can be applied to bibliometric research. The current version includes a suite of processing tools designed to take raw English language text input and output a complete textual analysis and linguistic annotation appropriate for higher-level textual analysis. The data for this project includes the title and abstract of all articles published in the Journal of the American Society for Information Science and Technology (JASIST) in 2012 (n = 177). Our process will provide an overview of the concepts depicted in the journal that year and will highlight the most frequent concepts to establish an overall trend for the year.


Text Mining Name Entity Recognition Document Cluster Bibliometric Research Bibliographic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Library and Information ScienceYonsei UniversitySeoulSouth Korea
  2. 2.Department of Information and Library Science, School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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