| Typicality: | 0.505 |
| Saliency: | 0.493 |
| to spot money laundering | 7 | purpose |
| for data mining | 7 | purpose |
| to identify customers’ spending patterns | 7 | purpose |
| bank → use → machine learning | 40 |
| bank → apply → machine learning | 8 |
| bank → leverage → machine learning | 8 |
| bank → turn to → machine learning | 7 |
| bank → introduce → machine learning | 4 |
| bank → invest in → machine learning | 3 |
| bank → use → machine learning algorithms | 3 |
| bank → adopt → machine learning | 3 |
| negative | neutral | positive |
| 0.074 | 0.572 | 0.354 |
| Raw frequency | 76 |
| Normalized frequency | 0.493 |
| Modifier score | 0.638 |
| Perplexity | 108.898 |