  Many research fields codify their findings in standard formats, often by
reporting correlations between quantities of interest. But the space of all
testable correlates is far larger than scientific resources can currently
address, so the ability to accurately predict correlations would be useful to
plan research and allocate resources. Using a dataset of approximately 170,000
correlational findings extracted from leading social science journals, we show
that a trained neural network can accurately predict the reported correlations
using only the text descriptions of the correlates. Accurate predictive models
such as these can guide scientists towards promising untested correlates,
better quantify the information gained from new findings, and has implications
for moving artificial intelligence systems from predicting structures to
predicting relationships in the real world.
