Though it’s doing more than just individual keyword stuff. I think one major point is that it’s looking at context (ie, I think it’s supposed to have at least a basic ability to deal with puns and such.)
Also, I think it is set up to learn the theme of a category if it’s not initially sure (via associated questions and answers), and using that info to get an idea of what types of answers are being sought in a particular category.
If it’s not parsing, if it’s just keyword analysis rather than any analysis of grammar, it’s going way beyond just judging the keywords individually. (Not to mention, it’s parsing enough to at least figure out which words are the ones to use for its keyword search, I think.)
Do you think that Watson is anywhere near the local maximum associated with the strategies you think is being used by that system, incidentally?
Having looked through their overview paper, I’m no longer sure. They do have modules that do parsing and semantic role labelling and such. But their model is a mixture of dozens of individual models. So it’s tough to say much about how things are fitting together. They use more sophisticated techniques than I thought, although I don’t know how much contribution those techniques actually make in the final decision.
Huh, thanks.
Though it’s doing more than just individual keyword stuff. I think one major point is that it’s looking at context (ie, I think it’s supposed to have at least a basic ability to deal with puns and such.)
Also, I think it is set up to learn the theme of a category if it’s not initially sure (via associated questions and answers), and using that info to get an idea of what types of answers are being sought in a particular category.
If it’s not parsing, if it’s just keyword analysis rather than any analysis of grammar, it’s going way beyond just judging the keywords individually. (Not to mention, it’s parsing enough to at least figure out which words are the ones to use for its keyword search, I think.)
Do you think that Watson is anywhere near the local maximum associated with the strategies you think is being used by that system, incidentally?
Having looked through their overview paper, I’m no longer sure. They do have modules that do parsing and semantic role labelling and such. But their model is a mixture of dozens of individual models. So it’s tough to say much about how things are fitting together. They use more sophisticated techniques than I thought, although I don’t know how much contribution those techniques actually make in the final decision.
Thanks for looking at the paper and passing on the info about what’s actually going in inside it, btw.