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Hi! Great work. I guess my question is - do you use 'averaging' of word vectors or the Chinese Restaurant process - to get to sub reddit vectors. You describe
by utunga 11y ago
Hi!
Great work. I guess my question is - do you use 'averaging' of word vectors or the Chinese Restaurant process - to get to sub reddit vectors. You describe the Chinese Restaurant process as a "more sophisticated method" that you "can" use, but in my experiments with word2vec and reddit (https://github.com/utunga/gensimred https://github.com/utunga/gensimred) I quickly discovered that simple averaging just does not work. Averaging has this awful 'revert to mean' thing that turns all the paragraph vectors into a sort of bland gray goo where they are all the same.
If you did use Chinese Restaurant process (I love that phrase - brings back memories of an occasion at a Dim Sum restaurant where this almost literally happened) it'd be great to see any source code you may feel like releasing ;_) ... well, it can't hurt to ask..
- gojomo 11y agoThe gensim library now offers the 'Paragraph Vector' [1] approach to create vectors for longer ranges of text. It's available in a class named Doc2Vec – but I don't think that's what is being used here. The Paragraph Vector approach can give interesting results for document-similarity, including similarity after certain 'algebraic'-like additions/subtractions of other topics/word-concepts. [2] [1] http://arxiv.org/abs/1405.4053 http://arxiv.org/abs/1405.4053 [2] http://arxiv.org/abs/1507.07998 http://arxiv.org/abs/1507.07998
- jmportilla 11y agoI used the gensim Doc2Vec implementation. You can check out some of the source code here: https://github.com/jmportilla/Reddit2Vec https://github.com/jmportilla/Reddit2Vec
- utunga 11y agoHi... Thanks for that. Awesome and much appreciated.