This is interesting stuff. I recall that at one time Google seemed to be heading in somewhat similar directions with Google Sets (now sadly gone -- I miss it).<p>I know that the author is looking squarely at use cases along the lines of a recommendation engine that would replace a human expert. But personally, I think it might be more interesting to examine things the algorithm can do that humans would find difficult or unintuitive. Sure, king - man + woman = queen is a very significant achievement; it's also obvious, to a human. Now, what can this algorithm come up with that is worthwhile, but that I would not find so obvious?<p>A couple of little comments:<p>> The algorithm eventually sees so many examples that it can infer the gender of a single word, ....<p>Do we really want to say that? Perhaps we should say that the algorithm is eventually able to make inferences that people would make based on knowledge of the gender of words -- which is not quite the same thing. (And again, I ask: what useful inferences can the algorithm make that humans would <i>not</i> make so quickly?)<p>> Despite the impressive results that come with word vectorization, no NLP technique is perfect. Take care that your system is robust to results that a computer deems relevant but an expert human wouldn't.<p>It should be noted that that "no NLP technique is perfect" idea applies to the NLP techniques used by human brains.