Text Classification via Universal Taxonomy - Looking for ML practitioners to test use-cases • /r/MachineLearning

It is made up of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, reviews, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. It is extremely accurate because we've built 55M controlled vocabularies (Ex. Being the noob I am, I am trying to understand how our real time classification capabilities can improve the efficiency of machine learned processes. I understand that supervised training models require a corpus of text from which a model can determine entities, ontological connections, and apply statistical models to understand what people, places, things, concepts are and how they may be connected, but we've already built out the taxonomy to understand connections between things, and can provide greater context to "what" something truly is.