11–20 of 168 posts
I'm surprised no one has made this yet with a clustered visualization.
Given that some matches are “mutual” and others are not, I don’t see how that could translate to a symmetric distance measure.
You have three points nearby, and a fourth a bit more distant. 4 best match is 1, but 1 best match is 2 and 3.
This is an interesting and well-written post but the data in the app seems pretty much random.
Thank you, tptacek. I was able to verify, thanks to the Internet Archive caching of "pg" for the post of 3 years ago, that the entries are quite similar in the case of "pg". Consider that it captures just the statistical patterns in very common words, so you are not likely to see users that you believe are "similar" to yourself. Notably: montrose may likely be a really be a secondary account of PG, and was also found…
I worked on a search engine for patents that used the first, our evaluations showed it was much better than other patent search engines and we had no trouble selling it because customers could feel the difference in demos.
I tried dimensional reduction on the BERT vectors and in all cases I tried I found this made relevance worse. (BERT has learned a lot already which is being thrown away, there isn't more to learn from my particular documents)
I don't think either of these helps with the "finding articles authored by the same person" because one assumes the same person always uses the same words whereas documents about the topic use synonyms that will be turned up by (1) and (2). There is a big literature on the topic of determining authorship based on style
https://en.wikipedia.org/wiki/Stylometry
[1] With https://sbert.net/ this is so easy.
This is an interesting and well-written post but the data in the app seems pretty much random.
Thank you, tptacek. I was able to verify, thanks to the Internet Archive caching of "pg" for the post of 3 years ago, that the entries are quite similar in the case of "pg". Consider that it captures just the statistical patterns in very common words, so you are not likely to see users that you believe are "similar" to yourself. Notably: montrose may likely be a really be a secondary account of PG, and was also found…
I tried my name, and I don't think a single "match" is any of my (very rarely used) throw away alts ;) I guess I have a few people I talk like?
I'm surprised no one has made this yet with a clustered visualization.
https://scikit-learn.org/stable/modules/generated/sklearn.ma...
I think other methods are more fashionable today
https://scikit-learn.org/stable/modules/manifold.html
particularly multi-dimension scaling, but personally I think tSNE plots are less pathological (they don't have as many of these crazy cusps that make me think it's projecting down from a higher-dimensional surface which is near-parallel to the page)
After processing documents with BERT I really like the clusters generated by the simple and old k-Means algorithm
https://scikit-learn.org/stable/modules/generated/sklearn.cl...
It has the problem that it always finds 20 clusters if you set k=20 and a cluster which really oughta be one big cluster might get treated as three little clusters but the clusters I get from it reflect the way I see things.
Earlier quoted context omitted.
Thank you, tptacek. I was able to verify, thanks to the Internet Archive caching of "pg" for the post of 3 years ago, that the entries are quite similar in the case of "pg". Consider that it captures just the statistical patterns in very common words, so you are not likely to see users that you believe are "similar" to yourself. Notably: montrose may likely be a really be a secondary account of PG, and was also found…
If you want to do document similarity ranking in general it works to find nearby points in word frequency space but not as well as: (1) applying an autoencoder or another dimensional reduction technique to the vectors or (2) running a BERT-like model and pooling over the documents [1]. I worked on a search engine for patents that used the first, our evaluations showed it was much better than other patent search engin…
https://antirez.com/hnstyle?username=dang&threshold=20&actio...
How does it find the high similarity between "dang" and "dangg" when the "dangg" account has no activity (like comments) at all? https://antirez.com/hnstyle?username=dang&threshold=20&actio...
My comments underindex on "this" - because I have drilled into my communication style never to use pronouns without clear one-word antecedents, meaning I use "this" less frequently that I would otherwise.
They also underindex on "should" - a word I have drilled OUT of my communication style, since it is judgy and triggers a defensive reaction in others when used. (If required, I prefer "ought to")
My comments also underindex on personal pronouns (I, my). Again, my thought on good, interesting writing is that these are to be avoided.
In case anyone cares.