Most commenters here find these demos very underwhelming and I must agree with them. Well known open source NLP packages like Stanford NLP or NLTK do much better on these (very academic) tasks.
I think the problem is that Watson's demos have been trained for very specific and narrow domains (like "cloud services" in the case of Resonance). What we really want to know is how easy (or difficult) it is to train Watson for our own domain.
If Watson has to make assumptions on my domain, then it will be really hard to build something useful with it.
I am not super familiar with Stanford NLP or NLTK, but I don't believe they overlap with more than two or three (relationship extraction, and maybe language id and some MT) of the services here. The value we want to bring is by providing all these services together in one place and provide tools to do fast domain adaptation. Right now to do adaptation you need to sign up to become an ecosystem partner but we will expose that adaptation tooling to everybody as well in the near future.
I'm by no means an expert, I'm just a linguist that's been learning python and NLTK for a little while. I would assume the demos don't show the potential of the technology. This kind of tech shines when you customize it and train it with domain-specific data, am I wrong?
I think the problem is that Watson's demos have been trained for very specific and narrow domains (like "cloud services" in the case of Resonance). What we really want to know is how easy (or difficult) it is to train Watson for our own domain.
If Watson has to make assumptions on my domain, then it will be really hard to build something useful with it.