  LeQua 2022 is a new lab for the evaluation of methods for "learning to
quantify" in textual datasets, i.e., for training predictors of the relative
frequencies of the classes of interest in sets of unlabelled textual documents.
While these predictions could be easily achieved by first classifying all
documents via a text classifier and then counting the numbers of documents
assigned to the classes, a growing body of literature has shown this approach
to be suboptimal, and has proposed better methods. The goal of this lab is to
provide a setting for the comparative evaluation of methods for learning to
quantify, both in the binary setting and in the single-label multiclass
setting. For each such setting we provide data either in ready-made vector form
or in raw document form.
