Towards Machine-assisted Meta-Studies: The Hubble Constant
This work addresses the challenge of efficiently analyzing large volumes of scientific publications for meta-studies in astrophysics, though it is incremental as it applies existing NLP techniques to a new domain.
The authors tackled the problem of automatically extracting measured values from astrophysical literature by developing a rules-based model that extracted 298 Hubble constant measurements from 208,541 arXiv papers, and they found a 3.5σ discrepancy in the data, demonstrating the tool's utility for meta-studies.
We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model -- a classical technique in natural language processing -- has successfully extracted 298 measurements of the Hubble constant, with uncertainties, from the 208,541 available arXiv astrophysics papers. We have also created an artificial neural network classifier to identify papers in arXiv which report novel measurements. From the analysis of our results we find that reporting measurements with uncertainties and the correct units is critical information when distinguishing novel measurements in free text. Our results correctly highlight the current tension for measurements of the Hubble constant and recover the $3.5σ$ discrepancy -- demonstrating that the tool presented in this paper is useful for meta-studies of astrophysical measurements from a large number of publications.