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  "title": "Following Up from Ignite ...",
  "note": "I keep meaning to post versions of the various talks I've given (the ones that are not videos) but I haven't yet, for myriad reasons ... including trying to get actual work done. But the Ignite talk I gave was pretty short (five minutes!), so I thought I'd try to put it up here.Slide 1: I always include a definition of 'lexicographer' when I talk; keeps people from elbowing their neighbor and asking 'what did she say she did again'?Slide 2: Because this was a tech talk, I also pointed out that I was a geek. (I wore this skirt, by the way, for visual reinforcement of the concept.)Slide 3: We all think of dictionaries as very concrete, solid objects. (You wouldn't want to drop one on your foot, would you?) But actually ... Slide 4: Dictionaries might be solid, but their innards are really collections of ABSTRACTIONS. Definitions are made by taking a lot of specific datapoints (uses of a word in context) ... Slide 5: ... and averaging them out to a more general meaning.Slide 6: This may seem really obvious to YOU ... [note: this is my favorite Flickr image EVER]Slide 7: But many people think that lexicographers just \"decide\" what a word means. Nope! That would be really hard work ... it's easier (and more accurate) to look at examples of use.Slide 8: What can you tell about this word [pirgate] from these examples? Well, I bet you know it's a verb, that you can do it TO something, and that it's something you might not want to do. This is all information you know implicitly because you know how English works.Slide 9: How about now? Now you know this use is a noun, and it's a kind of person -- the kind of person you don't want to be, probably. Slide 10: The truth is that \"meaning\" is created by lots of little points of data, in the same way that persistence-of-vision effects are created by lots of little points of light. A diffuse set of data can look pretty solid if it moves fast enough ...Slide 11: But you really do have to have ENOUGH data for this persistence-of-vision effect to work. If I say a guy is wearing a tux and holding a martini, is he a waiter, or is he James Bond? You don't have enough data to tell.Slide 12: So lexicographers in their labs try to distill all those usage data points into high-octane liquid definitions. Slide 13: Now, if you want to brew your own, and want a lot of examples of use, there are more places than ever to try to find them ... Slide 14: Although it doesn't work for every word ... (especially not 'pirgate', since I made it up).Slide 16: And, of course, the examples you DO find might not be helpful.Slide 16: The big question, though, isn't really WHERE to find enough examples -- that's pretty straightforward. The big question is: if we agree that dictionary definitions are abstractions of meaning, is there a better way to represent those abstractions than this:Slide 17: ... the boring old print dictionary?Slide 18: Could we show relationships between examples in a less-linear way? Slide 19: Could we convey those abstractions in a more powerful way? Slide 20: That's the problem I'm working on every day ... [go visit my blog, yadda yadda, big plug for Creative Commons and the awesome nice sharing people on Flickr.] (All my presentations are Creative Commons-sharealike, by the way, so if you ever want to remix me into a rap song or create a dictionary-talk novel or whatever, go right on ahead. If you see me speak somewhere and want a copy of my slide deck, just email and ask.]And that's what I talked about at Ignite last Thursday. More or less. I didn't make any notes, so this is from what I remember of what I said off the top of my head!",
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