  Creating a linguistic resource is often done by using a machine learning
model that filters the content that goes through to a human annotator, before
going into the final resource. However, budgets are often limited, and the
amount of available data exceeds the amount of affordable annotation. In order
to optimize the benefit from the invested human work, we argue that deciding on
which model one should employ depends not only on generalized evaluation
metrics such as F-score, but also on the gain metric. Because the model with
the highest F-score may not necessarily have the best sequencing of predicted
classes, this may lead to wasting funds on annotating false positives, yielding
zero improvement of the linguistic resource. We exemplify our point with a case
study, using real data from a task of building a verb-noun idiom dictionary. We
show that, given the choice of three systems with varying F-scores, the system
with the highest F-score does not yield the highest profits. In other words, in
our case the cost-benefit trade off is more favorable for a system with a lower
F-score.
