Yeah but what does label the data properly mean? If your high value samples are very sparse you will use some form of sampling usually for 'proper' labelling. Weak supervision can be a sampling strategy fundamentally.

I have used weak supervision with semi-supervised topic models for sampling where it worked very well.

The other largest impact area is using ontologies to extract ontology entities at scale and looking at the distribution of these entities for the problem you are working on. For example in pharma if you are trying to find a DRUG treats DISEASE relationship you might use an ontology to find all DRUG, DISEASE entities in Pubmed abstracts and pull all of them when they cooccur with the treats verb.

For my current work I apply weak supervision for information extraction for sales transcripts. Hopefully will be able to share some of the impact of this at the end of the quarter!