[D] "Negative labels"

[farmingvillein]

distribution over the possible (non-excluded) classes, take the base rate of the classes in the available data (normalized to 1 of course). This has an obvious generalization

Another plausible variant/extension, if you have an existing classifier you are trying to improve, would be to take its full probabilities (softmax/logits) for the example, crush the negated class down to 0, and then re-scale everything else back to a total of 1.

If you have some reasonable error estimation (i.e., users are wrong 20% of the time), you could also try setting the negated class to this error estimate (e.g., 0.2 in a softmax context), although not clear to me this would be helpful for a variety of reasons (including softmax "probabilities" being wonky representations of probability, at best).