Providing Immediate Context to Extracted Entities • /r/MachineLearning

I'm looking for help/direction for the use of a text classification engine powered by universal taxonomy in making certain ML processes more efficient through providing context to entities extracted from a corpus in real time. My company, eContext, has curated a universal taxonomy over the past nine years that encompasses everything commercially and socially relevant on the web. 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.