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Colorful Twitterbots

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The names we give to colors tell us as much about the namer as about the color itself. With so many hues and shades to choose from, a good color needs a good name. Just look at how paint companies like Dulux name their colors. Can we build a software agent, in the form of a Twitterbot, that invents meaningful and apt (if mischievous and sometimes rude) names for real colors? This presentation outlines the task of building a lexical invention system that assigns clever new names to colors and tweets the results on Twitter.

Publicada em: Engenharia
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Colorful Twitterbots

  1. 1. @everycolorbot is a minimalist and very popular exponent of the Mere Generation approach. Simply, the bot generates a random color hex-code (six hex digits denoting an R-G-B color code) and a picture of the corresponding color. Just as Borges’ library of Babel provided every possible book, this bot will eventually tweet every RGB color.
  2. 2. To go beyond mere generation, we need to attach semantics to our data, to guide a bot’s decisions. Consider @everycolorbot – Mike Cook suggests a CC bot that would creatively name its own colors, in a witty parody of Dulux paint names. Dulux uses pretentious names with positive effect, but a bot might call this one “mushrooms on toast” or “rusty battleship”. Such a bot would exhibit humor and visual appreciation. This twitterbot could respond to @everycolorbot with novel names, in a game of dueling bots.
  3. 3. We can blend new colors from old, by mathematically mixing the RGB codes of each ingredient. So two questions then: 1. What mixing ratio should be use when blending RGB codes? 2: How do we choose the colors to blend in the first place? This is actually the key question in any combinatorial view of creativity: how do we choose the elements to combine?
  4. 4. 25% Chocolate-brown 75% Sky-blue 25% Sky-blue 75% Chocolate-brown Here “chocolate” is the modifier and “sky” is the head. So the base color is sky-blue (75%), with an added hint of brown (25%). Here “sky” is the modifier and “chocolate” is the head. The base is chocolate brown (75%) with a hint (25%) of blue.
  5. 5. We use a large mapping of color stereotypes to specific RGB color codes.
  6. 6. We can use Web n- grams to suggest attested shades and tints of our color stereotypes, such as “Winter green” and “silver red”. Attested combinations make more sense than random combinations.
  7. 7. We can also use Web bi-grams to suggest attested combinations of our stereotypes. Q: Does Web Frequency serve as a measure of conventionality?
  8. 8. The lower the Web frequency, then the less conventional the combination ... … so the more striking and unusual the resulting color name.
  9. 9. Check out for the RGB codes of our color stereotypes.
  10. 10. contains Web- attested stereotype pairings. To minimize noise, these are bracketed with syntax elements to ensure well-formedness.
  11. 11. contains more Web- attested stereotype pairings. Not every bigram of color stereotypes is intended as a modifier-head phrase, so beware noise.
  12. 12. Check out for solid compounds of color stereotypes. These attested unigrams can be split into two parts, each a different color stereotype!
  13. 13. contains mod-head stereotype pairings with a plural head word. The singular form of the head stereotype is given in this column here.
  14. 14. Finally, check out for a corpus of this bot’s tweets. You can use the map of RGB codes to URLS to reuse the bot’s color swatches, so no need to create your own!

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