Very interesting perspective around the difficulty of learning weak labels. If I have time would be good to do a longer form write up around how effective skweak is for span extraction with it's hidden markov model approach for span extraction.

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!

I think the devil is in the details. You can use weak supervision to sample from a particular distribution and make your labelling more efficient.

It also works really well in pharma where you can build and apply ontologies for your weak supervision. In this case annotation would still be hard and required but your annotations would also be structured and adapted for later use in the ontology at the cost of slower annotation.

Point taken about the advert style writing, thanks for the feedback. My goal with the post is seeing what others do for weak supervision for NLP. I also think it's an underappreciated topic and would like to see more discussions around it.