Based on the feature description provided (employment, Present employment, in number of years), it appears that this feature represents the duration of present employment of a person.

To analyze the relationship between this feature and the task of whether the person receives a credit or not, we can examine the distribution of employment values for each target class (yes or no) and identify any patterns or trends.

Here is the dictionary representing the relationship between the feature "employment" and the target variable:

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

Explanation:
- For the "yes" class (person receives a credit), the possible values of the employment feature are "1<=X<4", "4<=X<7", and ">=7". This suggests that individuals who have been employed for 1 to 3 years, 4 to 6 years, or for more than 7 years are more likely to receive credit.
- For the "no" class (person does not receive a credit), the possible values of the employment feature are "unemployed" and "<1". This implies that individuals who are unemployed or have been employed for less than 1 year are less likely to receive credit.

Please note that all values from the original feature categories have not been included in the dictionary, as some values may not have a strong relationship with the target variable or may be hard to predict accurately based solely on the provided information.