Examining influential factors for acknowledgements classification using supervised learning

Acknowledgements have been examined as important elements in measuring the contributions to and intellectual debts of a scientific publication. Unlike previous studies that were limited in the scope of analysis and manual examination. The present study aimed to conduct the automatic classification o...

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Bibliographic Details
Published in:PloS one 2020-02, Vol.15 (2), p.e0228928-e0228928
Main Authors: Song, Min, Kang, Keun Young, Timakum, Tatsawan, Zhang, Xinyuan
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Automatic classification
Bibliometrics
Biology and Life Sciences
Cable television broadcasting industry
Categories
Citation analysis
Classification
Co authorship
Computer and Information Sciences
Data mining
Datasets
Deep learning
Finance
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Humans
Learning algorithms
Libraries
Library and information science
Machine Learning
Physical Sciences
Publications - classification
Publications - trends
Research and Analysis Methods
Research Personnel
Scholarly communication
Scholars
Social Sciences
Studies
Supervised learning
Supervised Machine Learning
Technical services
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Summary:Acknowledgements have been examined as important elements in measuring the contributions to and intellectual debts of a scientific publication. Unlike previous studies that were limited in the scope of analysis and manual examination. The present study aimed to conduct the automatic classification of acknowledgements on a large scale of data. To this end, we first created a training dataset for acknowledgements classification by sampling the acknowledgements sections from the entire PubMed Central database. Second, we adopted various supervised learning algorithms to examine which algorithm performed best in what condition. In addition, we observed the factors affecting classification performance. We investigated the effects of the following three main aspects: classification algorithms, categories, and text representations. The CNN+Doc2Vec algorithm achieved the highest performance of 93.58% accuracy in the original dataset and 87.93% in the converted dataset. The experimental results indicated that the characteristics of categories and sentence patterns influenced the performance of classification. Most of the classifiers performed better on the categories of financial, peer interactive communication, and technical support compared to other classes.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0228928