Abstract:
"Scientific article publications gained rapid growth in the recent decade due to
digitalization. Publications companies, researchers and article readers are concerned about
the content discoverability of the articles. This leads to the essential need for efficient
extraction of insights from data of the articles. To make the search easier and more relevant,
and improves user experience by proper recommendation, it is important to classify article
abstract more efficiently.
During the past few years, deep learning pre-training models have led to remarkable
breakthroughs for natural language processing. The proposed implementation is a
supervised machine learning-based approach. The research was carried out to determine if
it was possible to use a multi-labelled article abstract pre-labelled data with existing BERT
pre-trained model which were trained on the scientific domain in the form of transfer
learning, without compromising the accuracy and performance of the machine learning
model. The resulting research outcome was a ScholarBERT deep learning-based pretraining model which is used as a core for an article classification system, in which
research domain experts and research have the capability to identify the article categories
of given scientific article. An overall accuracy of 82% was achieved during the testing
phase of the created ScholarBERT model."
