Based on prior knowledge, we can analyze the relationship between the feature "month" and the target variable "Does this client subscribe to a term deposit?".

Since the feature represents the last contact month of the year, it is reasonable to assume that the month of contact could potentially have an impact on whether or not the client subscribes to a term deposit. It is possible that certain months are associated with better or worse outcomes in terms of subscription.

To generate the dictionary, we will examine the distribution of the feature "month" for each target class ("yes" or "no") and include only the list of months that have a non-empty count for each class.

Based on this analysis, the dictionary would look like:

```json
{
	"no": ["may", "jun", "jul", "aug", "oct", "nov", "dec"],
	"yes": ["mar", "apr", "sep"]
}
```

This dictionary represents the possible values of the feature "month" for each target class "no" and "yes".