Based on prior knowledge, the relationship between the feature "employment" and the task of determining whether a person receives credit can be analyzed as follows:

It is expected that individuals who have a stable and long-term employment history (e.g., ">=7" years) are more likely to receive credit compared to those who are unemployed or have a shorter employment duration. On the other hand, individuals with a very short employment duration (e.g., "<1" year) may also have a lower chance of receiving credit due to the perceived lack of stability.

Based on this analysis, the dictionary can be generated as follows:

```json
{
	"yes": [">=7", "1<=X<4", "4<=X<7"],  
	"no": ["unemployed", "<1"]
}
```

This means that individuals in the "yes" class (who receive credit) can have any of the following employment values: ">=7", "1<=X<4", or "4<=X<7". Individuals in the "no" class (who do not receive credit) can have "unemployed" or "<1" as the employment value.

Note that categories with less predictable outcomes, such as those that are difficult to determine based on prior knowledge, are excluded from the dictionary to ensure that the list of each target class is not empty.