Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks.

ABSTRACT: Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neur

SUBMITTER: Kandimalla B 

PROVIDER: S-EPMC8025978 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature