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Geoinference of author affiliations using NLP-based text classification.

Brian Lee1, John S Brownstein2,3, Isaac S Kohane4

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. brian@kimlee.org.

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Summary

This study introduces a natural language processing (NLP) model to accurately extract author locations from affiliations for bibliometric analysis. The NLP model overcomes limitations of traditional geoparsing tools, improving research network analysis.

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Area of Science:

  • Bibliometrics
  • Computational Linguistics
  • Geoinformatics

Background:

  • Author affiliations are vital for bibliometric studies, necessitating accurate location extraction.
  • Existing geoparsing tools struggle with unstructured affiliations, leading to errors and inefficiency in large-scale analyses.
  • Current machine learning geoparsers require explicit location data, limiting their applicability.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) model for predicting city, state, and country from free-text author affiliations.
  • To automate location inference, overcoming the limitations of traditional and existing machine learning geoparsing methods.

Main Methods:

  • Developed a natural language processing model utilizing text classification techniques.
  • Employed the LinearSVC algorithm for training and prediction.
  • Validated the model using the MapAffil dataset and additional public datasets.

Main Results:

  • The NLP model accurately infers high-resolution author locations, including city, state, and country.
  • Achieved superior accuracy compared to existing methods across multiple validation datasets.
  • Demonstrated effective location retrieval even when explicit geographic data is absent in affiliations.

Conclusions:

  • The developed NLP model offers a robust solution for automated geographical location inference from author affiliations.
  • This advancement significantly benefits bibliometric studies, research network analysis, and understanding global research distribution.
  • Highlights the practical application of text classification in extracting specific geographical data for scholarly analysis.