First order logic (FOL) is good for expressing simple facts like "all birds have wings" or "no bird has hair", but not for statements like "most birds can fly". To do that you have to at least extend it with fuzzy logic (probability and confidence).
A second problem is, how do you ground the terms? If you have "for all X, bird(X) => has(X, wings)", where does "bird", "wings", "has" get their meanings? The terms do not map 1-1 to English words, even though we may use the same notation. For example, you can talk about the wings of a building, or the idiom "wing it". Most words in the dictionary list several definitions that depend on context. Also, words gradually change their meaning over time. I think FOL represents complex ideas poorly. Try translating what you just wrote into FOL and you will see what I mean. -- Matt Mahoney, [EMAIL PROTECTED] ----- Original Message ---- From: Philip Goetz <[EMAIL PROTECTED]> To: agi@v2.listbox.com Sent: Tuesday, November 28, 2006 5:45:51 PM Subject: Re: [agi] Understanding Natural Language Oops, Matt actually is making a different objection than Josh. > Now it seems to me that you need to understand sentences before you can > translate them into FOL, not the other way around. Before you can translate > to FOL you have to parse the sentence, and before you can parse it you have > to understand it, e.g. > > I ate pizza with pepperoni. > I ate pizza with a fork. > > Using my definition of understanding, you have to recognize that "ate with a > fork" and "pizza with pepperoni" rank higher than "ate with pepperoni" and > "pizza with a fork". A parser needs to know millions of rules like this. Yes, this is true. When I said "neatly", I didn't mean "easily". I mean that the correct representation in predicate logic is very similar to the English, and doesn't lose much meaning. It was misleading of me to say that it's a good starting point, though, since you do have to do a lot to get those predicates. A predicate representation can be very useful. This doesn't mean that you have to represent all of the predications that could be extracted from a sentence. The NLP system I'm working on does not, in fact, use a parse tree, for essentially the reasons Matt just gave. It doesn't want to make commitments about grammatical structure, so instead it just groups things into phrases, without deciding what the dependencies are between those phrases, and then has a bunch of different demons that scan those phrases looking for particular predications. As you find predications in the text, you can eliminate certain choices of lexical or semantic category for words, and eliminate arguments so that they can't be re-used in other predications. You never actually find the correct parse in our system, but you could if you wanted to. It's just that, we've already extracted the meaning that we're interested in by the time we have enough information to get the right parse, so the parse tree isn't of much use. We get the predicates that we're interested in, for the purposes at hand. We might never have to figure out whether pepperoni is a part or an instrument, because we don't care. ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
