Based on the given feature "employment, Present employment, in number of years" and the task of determining whether the person receives a credit or not, we can analyze the relationship between the feature and the target variable.

A higher number of years in present employment may indicate the stability of the person's job, which could increase the likelihood of receiving credit. On the other hand, being unemployed or having a very short duration of employment may indicate a higher risk and could decrease the chances of receiving credit.

Based on this analysis, we can generate the dictionary as follows:

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

In this case, we only include the categories that have a clear relationship with the target variable. For the categories that are hard to predict or don't have a clear impact on the target variable, we do not include them to keep the lists for each target class from being empty.