Article-level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling.

ABSTRACT: Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of "deep learning" approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications-the es

SUBMITTER: Rivest M 

PROVIDER: S-EPMC8112690 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature