  Many ways of annotating a dataset for machine learning classification tasks
that go beyond the usual class labels exist in practice. These are of interest
as they can simplify or facilitate the collection of annotations, while not
greatly affecting the resulting machine learning model. Many of these fall
under the umbrella term of weak labels or annotations. However, it is not
always clear how different alternatives are related. In this paper we propose a
framework for categorising weak supervision settings with the aim of: (1)
helping the dataset owner or annotator navigate through the available options
within weak supervision when prescribing an annotation process, and (2)
describing existing annotations for a dataset to machine learning practitioners
so that we allow them to understand the implications for the learning process.
To this end, we identify the key elements that characterise weak supervision
and devise a series of dimensions that categorise most of the existing
approaches. We show how common settings in the literature fit within the
framework and discuss its possible uses in practice.
