Its good to dream, and we've come up with a lot of interesting ideas. But I think at this point we need some basic implementation before we can really go into details. A simple hack that parses a pre-defined set of keywords from a specially tagged comment in a help patch would be relatively easy to implement. Then we can test it out, and take it from there.
So... the question remains, who actually wants to do some implementation? I am sure I'll try my hand at this at some point, but I have no idea when.
.hc
On Apr 25, 2005, at 9:15 AM, Bryan Jurish wrote:
morning folks,
Just to add my -b¤ 0.02, I think Ben's idea of an integrated help-file-A structuring and searching mechanism would be quite useful -- I'm currently using a perl script and grep to index my own abstractions along similar lines (topmost comment in the file together with some rather arbitrary string matching conventions which probably only apply for me), but a more comprehensive catalog, especially for builtin objects and externals, would be very helpful indeed.
On 25 April 2005 at 14:05:18, IOhannes m zmoelnig wrote:
yes, i still think that full-object-browsing will lead to information-overdose if we don't limit ourselves to some structuring mechanism, which in turn i think will be problematic (given the different characters of pd-developers)
Agreed. Still, I think the option of adding one's own (arbitrary) keywords / structuring conventions is important too -- pre-defined hard categorization schemes have a disturbing tendency to go all goopy at the edges when they're not dictatorially enforced, which I don't think anyone here really wants to do or to be done; maybe the solution is just as simple as differentiating between "browsing" and "searching"?
probably we should ping bryan, as he might be the one with most knowledge on auto-clustering words.
Consider me ping'd [icmp_seq=1 ttl=64 time~=3600000 ms] ;-)
As it turns out, I happen currently to be working on an unsupervised word clustering system, although not directly along semantic lines. Still, there are methods for generating full-blown hierarchies and/or "flat" clusters based only on, say, word co-occurrences. There's even a nifty technique to find the most salient dimesions (highest variance) in a feature data space. Problems include (as you might expect): unsupervised clustering doesn't necessarily get you meaningful groupings; and perhaps more importantly from a user standpoint, it doesn't get you meaningful cluster labels (there are ways to work around this too, but they don't solve the basic problem); lastly, auto-clustering is computationally very expensive -- I might try running documentation comments through a co-occurence clustering algorithm, but I don't see any of these techniques becoming really useful at runtime -- at most we could use them to help induce an initial breakdown for existing objects...
marmosets, Bryan
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