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This study presents an ensemble approach that addresses the challenges ofidentification and analysis of research articles in rapidly evolving fields,using the field of Artificial Intelligence (AI) as a case study. Our approachincluded using decision tree, sciBERT and regular expression matching ondifferent fields of the articles, and a SVM to merge the results from differentmodels. We evaluated the effectiveness of our method on a manually labeleddataset, finding that our combined approach captured around 97% of AI-relatedarticles in the web of science (WoS) corpus with a precision of 0.92. Thispresents a 0.15 increase in F1 score compared with existing search term basedapproach. Following this, we analyzed the publication volume trends and commonresearch themes.We found that compared with existing methods, our ensembleapproach revealed an increased degree of interdisciplinarity, and was able toidentify more articles in certain subfields like feature extraction andoptimization. This study demonstrates the potential of our approach as a toolfor the accurate identification of scholarly articles, which is also capable ofproviding insights into the volume and content of a research area.

An Ensemble Approach for Research Article Identification: a Case Study in Artificial Intelligence