Oxford Deep NLP – An advanced course on natural language processing
here is what I don't understand about deep NLP (please keep in mind that I just began exploring this field):I am currently working on an algorithm that uses elementary text cues in combination with large data-table lookups to determine things like relevant keywords of news articles scraped from various sites. I have given my results to hundreds of people independently to provide me with some feedback regarding the quality. Here is the current breakdown:80% of the cases I get perfect score.10% of the cases I get acceptable score.10% of the cases needs improvement.My questions here are:1. if deep nlp can only provide us with the same level of efficiency/accuracy, then why the hell would we use it?2. if deep nlp can provide us with more efficiency than what is stated above then wouldn't it be safe to assume that is UNREASONABLY efficient?3. why are most people using deep nlp or ML in general right off the bat. Theoretically, it would be far more interesting to construct a model where the result of a statistical/linguistically parsing is fed to some sot of ML algo in order to tackle that 10% of bad cases.