Based on prior knowledge, we can analyze the relationship between the "employment" feature and the target variable of whether a person receives a credit or not.

Considering the categories of the "employment" feature:
- ">=7" represents employment for more than or equal to 7 years
- "1<=X<4" represents employment for 1 to 3 years
- "4<=X<7" represents employment for 4 to 6 years
- "unemployed" represents the applicant being unemployed
- "<1" represents employment for less than 1 year

Based on this, we can generate a dictionary with the possible values of the "employment" feature for each target class:

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

Here, the possible values for the "employment" feature for the target class "yes" are ">=7", "1<=X<4", "4<=X<7", "<1". For the target class "no", the possible value is "unemployed".