ESMARConf2021 Session 1A (Automation (text analysis)): Richard Cornford
Presenter: Richard Cornford
Session: Automation (text analysis) ( • ESMARConf2021 livestream Session 1A -... )
Title: Automated identification of articles for ecological datasets
Abstract: Synthesising data from multiple studies is necessary to understand broad-scale ecological patterns. However, current collation methods can be slow, involving extensive human input. Given rapid and increasing rates of scientific publication, manually identifying data sources amongst hundreds of thousands of articles is a significant challenge. Automated text-classification approaches, implemented via R and Python, can substantially increase the rate at which relevant papers are discovered and we demonstrate these techniques on two global biodiversity indicator databases. The best classifiers distinguish relevant from non-relevant articles with over 90% accuracy when using readily available abstracts and titles. Our results also indicate that, given a modest initial sample of just 100 relevant papers, high performing classifiers could be generated quickly through iteratively updating the training texts based on targeted literature searches. Ongoing work to facilitate the wider application of these methods includes the development of an easy-to-use Shiny App/R package and named-entity-recognition to assist the screening procedure. Additional research will also help to identify/mitigate potential biases that automated classifiers could propagate and evaluate model performance in other domains of evidence synthesis.
Please cite as: Cornford, Richard (2021): Automated identification of articles for ecological datasets. Presentation at ESMARConf2021. figshare. Conference contribution. https://doi.org/10.6084/m9.figshare.1...
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