  This study presents an ensemble approach that addresses the challenges of
identification and analysis of research articles in rapidly evolving fields,
using the field of Artificial Intelligence (AI) as a case study. Our approach
included using decision tree, sciBERT and regular expression matching on
different fields of the articles, and a SVM to merge the results from different
models. We evaluated the effectiveness of our method on a manually labeled
dataset, finding that our combined approach captured around 97% of AI-related
articles in the web of science (WoS) corpus with a precision of 0.92. This
presents a 0.15 increase in F1 score compared with existing search term based
approach. Following this, we analyzed the publication volume trends and common
research themes.We found that compared with existing methods, our ensemble
approach revealed an increased degree of interdisciplinarity, and was able to
identify more articles in certain subfields like feature extraction and
optimization. This study demonstrates the potential of our approach as a tool
for the accurate identification of scholarly articles, which is also capable of
providing insights into the volume and content of a research area.
