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):<p>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:<p>80% of the cases I get perfect score.<p>10% of the cases I get acceptable score.<p>10% of the cases needs improvement.<p>My questions here are:<p>1. if deep nlp can only provide us with the same level of efficiency/accuracy, then why the hell would we use it?<p>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?<p>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.