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They're NNs because you learn the representation using RNNs. Everything afterwards is trivial since you're in a hilbert space. But getting the representations i
by ves 10y ago
They're NNs because you learn the representation using RNNs. Everything afterwards is trivial since you're in a hilbert space. But getting the representations is the hard part.
- RockyMcNuts 10y agooh, ok. Do you have to use RNNs? I think I've done them without RNNs. Would love a good RNN word2vec type example with Tensorflow if anyone knows one.
- olh 10y agoOr you could use a pre-trained list like the ones from Google [1]. If not you probably solved an open problem in the area and publishing it would help us not to lose time trying to solve it again. [1] - https://code.google.com/archive/p/word2vec/ https://code.google.com/archive/p/word2vec/ Edit: word2vec on tensorflow tutorial https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/index.html https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/...
- RockyMcNuts 10y agoYeah, I implemented something based on the code from the Udacity course that Googlers (Vincent Vanhoucke) did on Tensorflow, basically same I think their version https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/udacity/5_word2vec.ipynb https://github.com/tensorflow/tensorflow/blob/master/tensorf... my version https://github.com/druce/streeteye_word2vec/blob/master/word2vec.ipynb https://github.com/druce/streeteye_word2vec/blob/master/word...
- nl 10y agoYou've seen https://www.tensorflow.org/versions/r0.9/tutorials/word2vec/index.html#vector-representations-of-words https://www.tensorflow.org/versions/r0.9/tutorials/word2vec/... ?
- rspeer 10y agoThat's standard word2vec, not an RNN.
- ot 10y agoword2vec does not use RNNs, the network is trained on a simple classification task "neighborhood" -> "word". Each word in the corpus is an independent example, there's no sequential dependence.
- eva1984 10y agoWord2vec doesn't use RNN. It has only one softmax layer after embedding.