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Comment by nfoz | Hacker News Reader

"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models."

Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something more to this buzzword than "slightly less shallow heuristic guesswork than the AI you already know and love".

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Deep learning is just a technique to do learning in (possibly many) layers.

And I disagree that trying to actually understand sentences is merely "slightly less shallow" than existing sentiment analysis systems (which, for the most part, treat sentences a just a bunch of words, count the positive and negative words, and take the average).

To possibly clarify, the way sentences are structured is itself learned, rather than relying on hard-coded rules.

Maybe you still find deep-learning disappointing, but there have been some successes from having the computer learn multiple levels of internal structure/representation from data.

The problem is that many sentences (at least in English) exhibit some structural ambiguity (more than one valid syntax tree). Also you have to take into account pragmatics and sociolinguistic factors (like variation), and so on. You can't just feed a formal grammar to a program and expect it to correctly parse English. We're still a LONG way from being able to correctly parse all (or even 99%) of possible English sentences.

It does not depend on hard-coded rules. It learns various probabilities for sentence structures. The result is much more flexible than a formal grammar.

True, but that still means you require some decent priors about the text that someone reading it might have. People generally have an idea of what the person who wrote it might be like, what sort of things to disregard, and so on. My point was that learning the "structure" of the language is not enough. I don't know exactly how this program works though, so it might be decent at that, I haven't read up on it enough.

The program doesn't need to know priors, if you make sure the priors for the test set are close to the same as the ones for the training set :)

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