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The NLM indexer assignment dataset: a new large-scale dataset for reviewer assignment research
Alastair R Rae1, James G Mork1, Dina Demner-Fushman1
1Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA.
Journal of the Association for Information Science and Technology
|February 23, 2023
Summary
The National Library of Medicine (NLM) created a dataset of 4.2 million article assignments to improve expert matching for MEDLINE indexing. This data supports better algorithms for assigning biomedical articles to qualified indexers.
Area of Science:
- Biomedical Informatics
- Information Science
- Medical Librarianship
Background:
- MEDLINE, the National Library of Medicine's (NLM) biomedical and life sciences journal article database, contains over 28 million references.
- All MEDLINE articles are indexed using NLM Medical Subject Headings (MeSH), requiring a large team of indexers to process nearly one million articles annually.
- Effective and timely indexing relies on accurately assigning articles to indexers with appropriate expertise.
Purpose of the Study:
- To introduce the NLM indexer assignment dataset, comprising 4.2 million article assignments from 2011-2019.
- To demonstrate the dataset's utility as a testbed for developing and evaluating expert matching and assignment algorithms.
- To explore the application of indexer article assignment data for domain-adaptive pre-training in related tasks, such as reviewer assignment.
Main Methods:
- The study introduces a novel dataset of 4.2 million indexer-article assignments.
- The dataset covers articles indexed within the MEDLINE database between the years 2011 and 2019.
- The dataset is utilized to evaluate expert matching and assignment algorithms.
Main Results:
- The NLM indexer assignment dataset serves as a valuable resource for testing expert matching and assignment algorithms.
- The process of assigning articles to indexers was found to be beneficial for domain-adaptive pre-training.
- This pre-training is applicable to related tasks, including the assignment of reviewers.
Conclusions:
- The NLM indexer assignment dataset provides a robust foundation for advancing research in expert matching and assignment systems.
- The findings highlight the potential of leveraging indexing assignment data for pre-training machine learning models in biomedical information science.
- This work contributes to improving the efficiency and accuracy of article indexing and reviewer selection processes within biomedical research.
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