An interesting answer is offered here:
https://github.com/dice-group/gerbil/wiki/Precision,-Recall-and-F1-measure
The authors of the module output different scores for precision and recall depending on whether true positives, false positives and false negatives are all 0. If they are, the outcome is ostensibly a good one.
In some rare cases, the calculation of Precision or Recall can cause a
division by 0. Regarding the precision, this can happen if there are
no results inside the answer of an annotator and, thus, the true as
well as the false positives are 0. For these special cases, we have
defined that if the true positives, false positives and false
negatives are all 0, the precision, recall and F1-measure are 1. This
might occur in cases in which the gold standard contains a document
without any annotations and the annotator (correctly) returns no
annotations. If true positives are 0 and one of the two other counters
is larger than 0, the precision, recall and F1-measure are 0.
I'm not sure if this type of scoring would be useful in other situations outside of their special case, but it's worth some thought.