Hacker News Reader

Comment by us-merul | Hacker News Reader

I really liked this article and these types of analyses always capture me. I just had to try out the game then. I nailed the link to "moon" from "rise" on my first try. Then I was a bit let-down for my first real task to get to "chill" starting from "chain." I went first to conglomerate, then corporation, then management... thinking I would at some point encounter "cold," and then "chill". Unfortunately not. Then I tried from chain to something like (my memory is imperfect here), necklace, jewelry, brilliance, glow, tranquil, calm-- and on a couple of other tries, appease, mollify, relax-- but could never get to "chill." I was able to win eventually by appealing to temperature which led me to chill.

Is there anything the user could do to modify the next steps, other than picking a word? Perhaps selecting some sort of valence related to metaphor or meaning? "I want to pick 'pacify', but in the sense of calming down, not to utterly destroy."

View on HN

Thanks for reporting on your experience! Those are good questions, and I will think about your valence idea for the future.

On a shorter horizon, I can tune the probability that on-path terms appear in the cloud. We store a larger pool of words than are displayed, and calculate lookaheads (and lookbacks from the target).

Maybe the user could type in their own words, and the app could approve/disapprove based on the 40 word list.

But maybe that adds an entirely new normalization function - user types 'runs' or 'ran', the app has to normalize to 'run'.

The app could just have a 'more words' button, loading the next 17.

Thanks for your response. Getting feedback like "hot or cold" in the algorithm's mind is exactly what I'm thinking of. It's a tricky issue and reminds me a lot of this: https://www.datcreativity.com/

I had tried hard to pick a set of fairly simple words, thinking I had an intricately unique association in my head, only to find out that the reported connections were nothing more than average. My partner obviously landed in an extremely high percentile by instantly picking the first words that came to her without much thought.

For good or bad, Semantle is able to report hot/cold because it's vector-based. We tried a few types of vectors, but I thought they were consistently unintuitive. So the best (and most relevant) proxy is remaining shortest distance-to-target, but often the player is only two hops away (spanning 17^2=289 options), and when they go astray and are much further, it's computationally too slow to look out more than 5 hops with brute force.

To chime in on Semantle - those vectors are often infuriating. High dimensional space truly doesn’t work the same as R3 or R2. I think a more human-oriented word vector database would make games like that more fun. I wonder if there’s a way to turn your codified data into an embedding that has more semantic value.

Thanks for that datcreativity.com link. My score was 94.11, higher than 99.88% of the people who have completed this task! I should hope so after working on relations for years. ;)

My words were: apple, shotgun, stardust, anger, hygiene, etymology, proctology, slant, dictator, and displacement.

Reply on news.ycombinator.com