2020
EMNLP
EMNLP 2020
The impact of preprint servers in the formation of novel ideas
Abstract
AbstractWe study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Interdisciplinary and Machine Learning
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Keyword Pioneer
— phrase appearance
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Hot Topic Early Bird
— time series analysis
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Statistical Learning
Healthcare & Medicine > Research > Bioinformatics
Interdisciplinary > Science > Digital Humanities
Machine Learning > Bayesian & Probabilistic > Bayesian Learning