ArticlePeerJ. Computer science2024
An ensemble approach for research article classification: a case study in artificial intelligence.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Opinion paper: generative AI and the future of scientometrics.Scientometrics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Text classification of research articles in emerging fields poses significant challenges due to their complex boundaries, interdisciplinary nature, and rapid evolution. Traditional methods, which rely on manually curated search terms and keyword matching, often lack recall due to the inherent incompleteness of keyword lists. In response to this limitation, this study introduces a deep learning-based ensemble approach that addresses the challenges of article classification in dynamic research areas, 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 support vector machine (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)
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.