SynonymoviePublished in 2004, Eugenio Tiselli’s Synonymovie takes any word you choose, finds images, and makes a “movie” of it. In order to begin you will also need Adobe Shockwave which can be found here. The algorithm in Synonymovie takes your word and finds an image related to the word using an online search engine, then a synonym of the word which is found using a web based synonym server, and presents you with the new word and image. Then the program repeats the process using the new word and continues to find synonyms and images until a word without synonyms is found.

The image sequences vary in length depending on the word you choose. Mt first choice was “green” and based on meaning of green I got a picture of a leaf, the word fleeceable and that was the end. I wasn’t happy with that so I tried green again and got a new list and set of images; green, jet, coal black, jet, achromatic, sable, habiliment, clothing – the end. I spend most of that list wondering how it went from green to jet, which was a huge turning point for the rest of the list. My conclusion was, green is a terrible word for this program, because it’s hard enough for a person to answer what “green” is, let alone a computer. A better word, was “literature” which I managed to get some pictures for below. Literature, writing, composition, oeuvre, work, play, go, disappear, melt, meld, rum, and cards.

The more I played with the program the more I enjoyed it and found the lists that were compiled from the internet (from wherever they may be- they’re not the first images/synonyms from a Google search) make interesting lists of associated words like; rose, wine, fuddle, confuse, piece, mend, mending, fixture, fixity, unchangeability, quality, level, destroy, and unmake. Just like green, if I re-enter rose I will get an entirely different list because of the random number generator, and I would be interested in seeing the numerous combinations of words and images and to have something to show for what these search engines perceive is synonymous with rose.

Based on the examples there were hits and misses which is true of a lot of information you find randomly on the internet. The more I look at the results, the more I think of this work as a criticism of the internet, and that this program emphasized – that based on the information in the search engines, the algorithm made widely different choices than what a person would. And it’s up to humans to interpret the information provided by the internet because sometimes search engines are unreliable…what do you think?

Literature         Writing        Composition

  4 comments for “Synonymovie

  1. Katie Diemer
    February 10, 2014 at 2:00 pm

    It seems that the game has an easier time interpreting nouns than other words – “rose” could be interpreted as a noun (a flower), an adjective (a color) or a verb (“He rose from the dead”). I think “jet” might relate to the color jet black instead of an airplane, but like you said, I’m still not sure how that relates to green besides color.
    Due to the fact that the words in the game can be interpreted in many ways, I’m not sure if this is a criticism of the format of search engines on the internet. I think it might be a portrayal of the multiple ways people interpret words (and the world around them). You might see the word “work” and think of a work of art, I might look at it and think of business. We both see the same word, but we think of it in different ways.
    Do you know if there’s a way to see all of the words you can use? It might be interesting to see how large/limited the list is.

  2. Bekka
    February 10, 2014 at 3:31 pm

    I messed around with this piece for a while, trying whatever words popped into my head and I thought it was interesting how long some chains would be compared to others. I had one that was 5 words long (fight – fighting – scrap – dispute – contravention)
    and then an 18 word long chain (end – change – replace – change – issue – communicate – communicate – pass – vanish – stop – interrupt – act – expression – demonstration – presentment – presentation – attitude – cognition) the repitition of words in this also interested me. Synonymovie seems to realize not only synonyms but words as objects and verbs or words with multiple meanings. It would be really interesting to find it algorithm it uses!

  3. kutoof
    February 14, 2014 at 5:10 pm

    These seems like a cool and fun way to mess around text and images. The summary you give on how to use the engine is simple and concise. They are good directions to follow and helpful as well. I’m glad you gave your readers the process that you went through when using the system. You show the difference between using “green” and “literature” and give an argument for the sake of the computer that there are a lot more synonyms and images to match “literature” then there is “green”. But I do want to argue that the computer should have linked “green” to money, trees, greed, and some vegetables. And this gives that the system might not be fully developed yet.

    I completely agree with your statement that is it up to people interpret information rather than a computer but then again, what kind of interpretations are we making? When it comes to reading literature, there is always that issue of whether or not people’s interpretations are right. So the real question is, who should judge these interpretations?

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