Based on prior knowledge, we can analyze the relationship between the occupation feature and the task of earning more than $50000 per year. 

There might be certain occupations that are more likely to earn above $50000 per year, such as 'Exec-managerial' or 'Prof-specialty', which are generally higher-paying jobs. On the other hand, we might expect occupations like 'Other-service' or 'Priv-house-serv' to have a lower likelihood of earning above $50000 per year.

After analyzing the dataset and considering the relation between the occupation feature and the target variable, we can generate a dictionary with the relevant information. Here is the generated dictionary in the required format:

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

Please note that the occupation values included in each target class are based on assumptions and general knowledge about income levels associated with different occupations.