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Comment by 19h | Hacker News Reader

We also trained on all of pushshift and have an average ”unknown” word rate of less than 0.007% — the Reddit corpus is rather amazing to capture pretty much all misspellings of a word.

We may only be using 16k vector values but that doesn’t mean we only have a vocab of 16k —- our vocab is more around 1.9 million words each described by a sparse fingerprint of 16k.

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I'm curious though, how do you handle related forms of a word (assuming you don't use stemming)? It doesn't seem to me that this process would automatically handle that.

i think they don't. given enough data those related forms will have similar colocation profiles (aka fingerprints). not a model of semantics.. at scale it might become a proxy though.

Yes, we don't actively cover this.

Our training dataset is composed of 12 different languages. The English corpus has a size of around 25TB -- ~18TB after a rather aggressive filtering process (char frequencies [i.e. https://gist.github.com/19h/b983769b7ec9c4a9528377d7819892a7], too many unusual features in the sentences, too short sentences, etc.).

Most common misspellings of words have nearly identical fingerprints, and we align the retinas across languages so that translated sentences result in similar fingerprints (via competitive learning).

"retina" describes the orientation of the vector space? first time i hear it in this context.

Apologies, that's a very semantic folding specific term -- with retina I refer to a word -> fingerprint database that gives you either fingerprints of single words or of blocks of text (by applying relevant heuristics to filter out irrelevant noise / heavily used words in a specific language).

thanks for the clarification. if your base population is that large then it's frequencies and you get a fingerprint. well done.

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