Based on prior knowledge, we can analyze the relationship between the feature "month" and the task of whether the client subscribes to a term deposit.

An analysis of the feature "month" suggests that there might be a relationship between the month of last contact and the client's decision to subscribe to a term deposit. Different months might exhibit varying levels of success in terms of convincing clients to subscribe.

To create the dictionary, we need to examine the different values of the "month" feature for each target class (yes and no).

Here is the process to create the dictionary:

1. Separate the dataset based on the target class (yes and no).

2. For the "yes" target class, identify the distinct values of the "month" feature where clients subscribed to a term deposit.

3. For the "no" target class, identify the distinct values of the "month" feature where clients did not subscribe to a term deposit.

4. Collect the distinct values for each target class in separate lists.

Based on this analysis, here is the resulting dictionary:

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

Please note that this is a sample dictionary based on hypothetical analysis. The actual values may vary depending on the dataset and the specific task.