Based on prior knowledge, the employment duration can be a significant factor in determining whether a person receives credit or not. Generally, people with a stable and longer employment history are considered more creditworthy by lenders.

To analyze the relationship between the feature "employment" and the task of whether a person receives credit or not, we can examine the distribution of different employment categories within each target class.

Here is the dictionary generated based on the analysis:

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

This dictionary splits the possible values of the "employment" feature into two lists based on the target class. The "yes" list includes the employment categories that are more likely to result in receiving credit, while the "no" list consists of employment categories associated with a lower chance of receiving credit.

It's worth noting that the "unemployed" and "<1" categories are mentioned in the "no" list to indicate that having either of these employment statuses might negatively impact the chances of receiving credit. 

Other employment categories not mentioned in the dictionary do not provide enough information to make a clear determination and are thus not included.