Based on prior knowledge, it is difficult to predict the relationship between occupation and the task of whether a person earns more than $50,000 per year. However, we can still analyze the feature and target relationship by examining the frequency of different occupations within each target class.

Here is the generated dictionary:

```json
{
	"no": [
		"Machine-op-inspct",
		"Farming-fishing",
		"Protective-serv",
		"Other-service",
		"Craft-repair",
		"Adm-clerical",
		"Tech-support",
		"Priv-house-serv",
		"Transport-moving",
		"Handlers-cleaners",
		"Armed-Forces"
	],
	"yes": [
		"Prof-specialty",
		"Exec-managerial",
		"Sales"
	]
}
```

In the "no" list, we have included the possible occupation values for individuals who earn less than or equal to $50,000 per year. In the "yes" list, we have included the possible occupation values for individuals who earn more than $50,000 per year. The values in both lists are based on the available occupation categories provided.