The scholarly publication space is growing steadily not just in numbers but
also in complexity due to collaboration between individuals from within and
across fields of research. This paper presents a hierarchical classification
system that automatically categorizes a scholarly publication using its
abstract into a three-tier hierarchical label set of fields
(discipline-field-subfield). This system enables a holistic view about the
interdependence of research activities in the mentioned hierarchical tiers in
terms of knowledge production through articles and impact through citations.
The classification system (44 disciplines - 738 fields - 1,501 subfields)
utilizes and is able to cope with 160 million abstract snippets in Microsoft
Academic Graph (Version 2018-05-17) using batch training in a modularized and
distributed fashion to address and assess interdisciplinarity and inter-field
classifications. In addition, we have explored multi-class classifications in
both the single-label and multi-label settings. In total, we have conducted
3,140 experiments, in all models (Convolutional Neural Networks, Recurrent
Neural Networks, Transformers), the classification accuracy is > 90% in 77.84%
and 78.83% 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, as well as to capture the degree of interdisciplinarity in a
publication which enables downstream analytics such as field
interdisciplinarity. This system (a set of pretrained models) can serve as a
backbone to an interactive system of indexing scientific publications.

通过利用抽象、将学术出版物自动分类为专业领域体系结构的一种基于模块化和分布式批量训练的层次分类系统 <discipline-field-subfield>，从而在知识生产和引用间捕捉相互依存的关系，进而更好地对学术出版物进行索引。