[igravious]

How about looking at it this way. We now have multiple implementations that get 94%+ success _without_ knowing anything about the world. Isn't that remarkable?

Now to get to 99.4%+ how about we combine techniques such as spaCy or Parsey McParseFace (love the name Google) with very simple real-world cognitive models. So for the example given "Alice drove down the street in her car." a simple cognitive model would _know_ that streets cannot be in cars and so be able to disambiguate. A cognitive model wouldn't know all the facts about the world, it would know certain things about streets, certain things about cars and be able to infer on the fly whether the relationship between streets and cars matches either the first parse possibility or the second. To me this seems like the obvious next step. If it's obvious to me it must have been obvious to someone else so presumably somebody is working on it.

[fighting]

The 94% success rate is in made up, limited tests. Real world, they fail constantly in weird horrific or laughable ways. See any Microsoft AI public demo ever. It's like the self driving car claim of millions of miles without accidents, except that humans took over anytime there was a chance of one.

[YeGoblynQueenne]

>> We now have multiple implementations that get 94%+ success _without_ knowing anything about the world. Isn't that remarkable?

That success only lasts in the limited context of the corpora used for training. Step outside that and success goes down to 60% or much worse. And that's just tagging and things, shallow parsing. Meanning? Discourse? Don't even think about it.

[adrianratnapala]

I suspect another way of looking at it is that these models actually learn about the real world by reading about it in the WSJ -- of course their knowledge of it is not as deep as our own, but good enough for what they do.

That is, if you took the well NLP trained model, then you could in principle extract out of it facts like "streets are not found inside cars".