--- Mike Tintner <[EMAIL PROTECTED]> wrote: > Matt: Semantic models learn associations by proximity in the training text. > The > degree to which you associate "snake" and "rope" depends on how often these > words appear near each other > > Correct me - but it's the old, old problem here, isn't it? Those semantic > models/programs won't be able to form any *new* analogies, will they? Or > understand newly minted analogies in texts? And I'm v. dubious about their > powers to even form valid associations of much value in the ways you > describe from existing texts. > > You're saying that there's a semantic model/program that can answer, if > asked,: > "yes - 'snake, chain, rope, spaghetti strand' is a legitimate/ valid series > of associations"/ "yes, they fit together" (based on previous textual > analysis) ?
Yes, because each adjacent pair of words has a high frequency of co-occurrence in a corpus of training text. > or: " the odd one out in 'snake/ chain/ cigarette/ rope" is 'cigarette'"? Yes, because "cigarette" does not have a high co-occurrence with the other words. > I have yet to find or be given a single useful analogy drawn by computers > (despite asking many times). The only kind of analogy I can remember here is > Ed, I think, pointing to Hofstader's analogies along the lines of "xxyy" > is like "xxxxyyyy". Not exactly a big deal. No doubt there must be more, > but my impression is that in general computers are still pathetic here. This simplistic vector space model I described has been used to pass the word analogy section of the SAT exams. See: Turney, P., Human Level Performance on Word Analogy Questions by Latent Relational Analysis (2004), National Research Council of Canada, http://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-47422.pdf -- Matt Mahoney, [EMAIL PROTECTED] ----- 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/?member_id=8660244&id_secret=71685861-05fe0f
