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

From the given feature description, the employment feature represents the number of years of present employment. It is a categorical variable with the following categories: ['>=7', '1<=X<4', '4<=X<7', 'unemployed', '<1'].

To analyze the relationship, we can look at the distribution of employment categories for both the "yes" and "no" classes.

Possible values of the employment feature for the target class "yes" (person receives credit) would be ['>=7', '1<=X<4', '4<=X<7', '<1']. People with different years of present employment might have different chances of receiving credit.

Possible values of the employment feature for the target class "no" (person does not receive credit) would be ['unemployed', '1<=X<4', '4<=X<7']. It's possible that people who are unemployed or have specific employment years might have a higher chance of not receiving credit.

Based on this analysis, we can create the following dictionary:

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

Note that the employment values that are hard to predict (e.g., 'unemployed') are included in the dictionary, as long as there is at least one example of such value in the corresponding target class.