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Replying to @listmouse @AmbrosialArts and
ty for the backup
@brazen_cabeza 10/10 mutual1 reply 0 retweets 2 likes -
Replying to @listmouse @AmbrosialArts and
sorry you still can’t construct a semantic discovery map by organizing according to lexical properties that isn’t how it works that isn’t how anything works
1 reply 1 retweet 7 likes -
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Replying to @listmouse @chaosprime and
don't worry guys I'm doing an analysis of all words appearing in wikipedia right now that should tell for sure whether @gypsy_panther is right or not
1 reply 0 retweets 5 likes -
Replying to @browserdotsys @chaosprime and
ok I regret to inform @gypsy_panther that she is wrong here
3 replies 0 retweets 6 likes -
Replying to @browserdotsys @chaosprime and
maximally efficient alphabetization will use the end of the word with the most information. we can measure this by computing the entropy of the distribution of first vs last characters for all words
1 reply 0 retweets 7 likes -
Replying to @browserdotsys @chaosprime and
In [72]: ent(first_char) Out[72]: 3.1136745915037434 In [73]: ent(last_char) Out[73]: 2.846924835789119
2 replies 0 retweets 5 likes -
Replying to @browserdotsys @chaosprime and
dataset: all words appearing in the title or text of an english wikipedia page, filtered as: filtered_words = [w for w in words if not (has_nonascii(w) or len(w) < 4 or digit_ratio(w) > .4)]
1 reply 0 retweets 6 likes
oh i thought you were proposing a dual index where we would look up a word in the L2R index or the R2L index based on whether that word’s first or last character had more entropy
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Replying to @chaosprime @browserdotsys and
sorry about increasing your monstrousness metric on that basis since it’s monotonic
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Replying to @chaosprime @AmbrosialArts and
I definitely like the high-grade autism implied by keeping two copies of every book in your collection so you can have two libraries with different sorting order
1 reply 0 retweets 13 likes - 8 more replies
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