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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Measuring novelty in science with word embedding.

Sotaro Shibayama1,2,3, Deyun Yin4,5, Kuniko Matsumoto3

  • 1School of Economics and Management, Lund University, Lund, Sweden.

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This study introduces a novel method to measure scientific article novelty using citation and text data. It quantifies novelty by analyzing semantic distances between cited references, offering a computationally efficient approach.

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Area of Science:

  • Bibliometrics
  • Scientific Communication
  • Information Science

Background:

  • Measuring scientific novelty is essential for understanding research impact.
  • Existing methods may have limitations in scope or accessibility.

Purpose of the Study:

  • To propose and validate a new approach for measuring the novelty of scientific articles.
  • To leverage both citation and text data for a comprehensive novelty assessment.

Main Methods:

  • Assigning word embeddings to cited references based on their text.
  • Computing semantic distances between pairs of references.
  • Summarizing reference distances to evaluate the focal document's novelty.

Main Results:

  • Validated the use of word embeddings for quantifying semantic distances.
  • Confirmed criterion-related validity against self-reported novelty scores.
  • Demonstrated the measure's ability to predict future citation impact.

Conclusions:

  • The proposed method offers a reliable and computationally efficient way to measure scientific novelty.
  • This approach requires minimal data and computational resources.
  • The developed code is publicly available for calculating novelty scores.