5 ms·

Try Pimantle[1], which is Semantle with a point-cloud visualization of guesses. The visualization is only two-dimensional, but it's enough to prevent the "orbit

• dr_orpheus 4y ago
  I do like this because you can see if the "path" you are on towards the center is going to get you close or if you are going to hit a dead end. Because it's weird some of the ways you can get relatively close to the word and then go nowhere. I have also found it frustrating in semantle when I get something that is "close" but is actually close because it is a direct antonym of the word.
• neogodless 4y ago
  Only took me about 70 guesses including a dozen hints. I'm not good at thinking like these algorithms...
  • deleted 4y ago
    [deleted]
• Der_Einzige 4y ago
  What dimensionality reduction algorithm is being used? PCA? UMAP? Of course better ones are likely to give better 2D representations. I've had this same idea to use the point cloud visualization to improve my own take on language model games - but I when I experimented with this, I found that even the best 2D representations were still quite bad in the context of trying to help out humans.
  • manimino 4y ago
    PCA destroys local structure at such a low dimensionality. So probably UMAP or T-SNE.
• 1270018080 4y ago
  My computer is on fire trying to run this. But it's way better than semantle
• anyfoo 4y ago
  Ah, indeed! Not much data yet, but with my first pimantle just now, I was able to get it in 33 tries, instead of often over a hundred with semantle. It really helps to "see" how you are approaching (or not approaching) the word in one more dimension.