A man went into a restaurant and ordered a hamburger. When the hamburger arrived it was burned to a crisp,– http://cogprints.org/7150/1/10.1.1.83.5248.pdf
and the man stormed out of the restaurant angrily, without paying for the hamburger or leaving a tip.” Now, if
you are asked -Did the man eat the hamburger?” you will presumably answer, ’ No, he did not.’ Similarly, if
you are given the following story: ‘-A man went into a restaurant and ordered a hamburger; when the
hamburger came he was very pleased with it; and as he left the restaurant he gave the waitress a large tip
before paying his bill,” and you are asked the question, -Did the man eat the hamburger?,-’ you will presumably
answer, -Yes, he ate the hamburger.” Now Schank’s machines can similarly answer questions about restaurants
in this fashion. To do this, they have a -representation” of the sort of information that human beings have about
restaurants, which enables them to answer such questions as those above, given these sorts of stories. When the
machine is given the story and then asked the question, the machine will print out answers of the sort that we
would expect human beings to give if told similar stories.
Percentage of github commit messages containing angry words by language.
via I love charts