Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Computer science,Artificial intelligence,Artificial neural network,Machine learning,Scientific method |
DocType | Volume | Citations |
Journal | abs/1805.06879 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James P. Bagrow | 1 | 281 | 26.25 |
| Daniel Berenberg | 2 | 0 | 0.68 |
| Joshua Clifford Bongard | 3 | 29 | 3.98 |