[jonmc12]

"Gibberish" - why? Many of the commercially used products that deal with representation of natural language use naive bayesian inference or statistical classification to return results.

Further, one of the promising approaches in this area involves using generative grammars (or other generative, non-parametric approaches) to approximate natural language representations.

Both these approaches 'compute' answers without a notion of natural language grammars that are usually associated with natural language processing.

[ivankirigin]

   usually associated
  

I've definitely read papers about using those methods you describe on processing text. They are all just algorithms attacking a problems, so the distinction between NLP and computing is gibberish.

[jonmc12]

Yes, I agree that all NLP must include some computational model. However, it is an interesting distinction to the reader of this article that the natural language engine is not based off of a linguistically derived grammar - like Powerset, and many of the larger, more notable NLP efforts.

This is the point the author was making. Had you not paraphrased the article to skew the intended meaning of this observation, it would have taken on a different meaning.

The actual text: "It doesn't simply parse natural language and then use that to retrieve documents, like Powerset... Instead, Wolfram Alpha actually computes the answers to a wide range of questions"

Computing the answer for 'what does a string of natural language mean' and 'what is the intended answer of the question being asked' are 2 different things.

[ivankirigin]

I see the distinction, but I think the review is light on content, so the criticism is fair. Why, pray tell, do we need to be hyped about a great new piece of software?

If it even close to real, the results will certainly speak for themselves. And the results might even be really awesome! I just would advise people to not pay attention until they see them.