Comments on squareone: Backronyms

I think there are better ways to improve the feel ...

2010-07-23T14:12:12.871-04:00

I think there are better ways to improve the feel of auto-generated backronyms. Sadly, with your proposed method humorous combinations like "floppy bazooka inc", for FBI becomes very unlikely and you're more likely to get: Federal Bureau of Investigations. No offense intended, but: bleah. There are acronym lookups for that.

You can better achieve the feel of autogenerated backronyms by making one of the words come from a predefined list of synonyms for "organization" (ex: admin, alliance, aristocracy, assn, bureau...), easily amassed from thesauruses and by combing through existing acronyms.

For example using that rule with "ABC", it would likely generate things like: ASSN of bloated catfish, aromatic BUREAU of clamminess, or anthropomorphic bowel CORP; In each case an organization synonym specified (in all-caps here for arguments sake). Note that it must be followed with "of" if it's not the last word.

Another easy to program rule that would really help: Every adjective must be followed by a noun. Another method: no verbs. Intelligently inserting verbs sounds like it could easily become a nightmare, I'd avoid them.

An improvement: http://www.jschramm.com/acreator/i...

2010-07-23T12:22:29.072-04:00

An improvement:
http://www.jschramm.com/acreator/index.php?term=anyword

To tweak result: click individual words within the result. Could be much better.

You'll have to excuse the SSL certificate chic...

2009-10-23T16:58:32.644-04:00

You'll have to excuse the SSL certificate chicanery, but yes there is a working demo here: https://labs.pheared.net/construct/backronym

Have you gotten anywhere with this. A program we c...

2009-10-19T09:51:47.609-04:00

Have you gotten anywhere with this. A program we can try out?

One thought I had is once the words are generated, an option to permanently eliminate particular words.........z

Look at you with all your parts of speech. Maybe y...

2009-03-29T09:24:00.000-04:00

Look at you with all your parts of speech. Maybe you should come to my school and explain to all my little darlings that even computer people need to know what adjectives are.

Miles, it's just this formula: positive_votes - ne...

2009-03-21T10:25:00.000-04:00

Miles, it's just this formula: positive_votes - negative_votes

So it's not a normal ratio, it's more of a measure of the balance between positive and negative. One side-effect of this method is that a project with 1000 positives and 1000 negatives sorts to the same place as one with 1 positive and 1 negative. I think that's what you want, but it's something to consider nevertheless.

Kevin, I am doing some Stimulus package research f...

2009-03-18T12:36:00.000-04:00

Kevin, I am doing some Stimulus package research for a Consultant. I am interested in leveraging the work you did in StimulusWatch.Org as part of a State (s) recovery site. This consultant has already done some work for DC. How can I find out more about the Project ratings?? Is it some formula around the % of Critical votes vs. Non Critical, # of votes, and jobs created? This would be helpful to know. Thanks, Miles

Thanks for the links Tim!

2009-03-02T21:01:00.000-05:00

Thanks for the links Tim!

http://www.lib.umich.edu/govdocs/fedexec.html#acro...

2009-02-28T02:45:00.000-05:00

http://www.lib.umich.edu/govdocs/fedexec.html#acronyms

http://ucblibraries.colorado.edu/govpubs/a-z/alpha.htm

Definitely an interesting approach. It looks like...

2009-02-25T18:38:00.000-05:00

Definitely an interesting approach. It looks like acronymfinder won't give up their list of 4 million acronyms for free (not that they are necessarily a good list, just numerous), but I see that wikipedia has a list that is ripe for the scraping. Maybe there are others...

I've been thinking about this and I think one of t...

2009-02-25T16:23:00.000-05:00

I've been thinking about this and I think one of the best ways to do it would be to take a list of existing acronyms (especially bureaucratic ones) and weight the generator so that it tends to use the words from the list more often. For example, this would mean "International" would come up much much more often for "I" than "Igloo."

Sort of how Google's translation stuff works; just take a corpus of good samples and use it and you come out looking much smarter (although occasionally dumb but in this case dumb would be funny and good).