  This paper presents a hierarchical classification system that automatically
categorizes a scholarly publication using its abstract into a three-tier
hierarchical label set (discipline, field, subfield) in a multi-class setting.
This system enables a holistic categorization of research activities in the
mentioned hierarchy in terms of knowledge production through articles and
impact through citations, permitting those activities to fall into multiple
categories. The classification system distinguishes 44 disciplines, 718 fields
and 1,485 subfields among 160 million abstract snippets in Microsoft Academic
Graph (version 2018-05-17). We used batch training in a modularized and
distributed fashion to address and allow for interdisciplinary and interfield
classifications in single-label and multi-label settings. In total, we have
conducted 3,140 experiments in all considered models (Convolutional Neural
Networks, Recurrent Neural Networks, Transformers). The classification accuracy
is > 90% in 77.13% and 78.19% of the single-label and multi-label
classifications, respectively. We examine the advantages of our classification
by its ability to better align research texts and output with disciplines, to
adequately classify them in an automated way, and to capture the degree of
interdisciplinarity. The proposed system (a set of pre-trained models) can
serve as a backbone to an interactive system for indexing scientific
publications in the future.
