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Tagged with word-embeddings softmax
5 questions
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How does softmax work for vectors?
In skipgram we predict the context words. That is the output layer before applying the softmax function is a number $V$ of words, where $V$ is the dictionary size. But each word is represented as a ...
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Word2vec/SkipGram: Why softmax?
In Word2Vec (SkipGram version), there is a softmax layer at the end of the neural net. As this is expansive to calculate, some approximations are used instead, such as negative sampling.
But if in the ...
- 103
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How does Word2Vec CBOW softmax work with multiple context words?
I'm referring to following paper from Xin Rong - "word2vec Parameter Learning Explained", to be precise the equation (4):
$$
p(w_j|w_I) = \frac{\exp(\mathbf{v’}^{T}_{w_{j}}\mathbf{v}_{w_{I}})...
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Does hierarchical softmax of skip gram and CBOW only update output vectors on the path from the root to the actual output word?
After reading word2vec Parameter Learning Explained by Xin Rong, I understand that in the hierarchical softmax model, there is no output vector representation for words, instead, each of the $V-1$ ...
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Why is hierarchical softmax better for infrequent words, while negative sampling is better for frequent words?
I wonder why hierarchical softmax is better for infrequent words, while negative sampling is better for frequent words, in word2vec's CBOW and skip-gram models. I have read the claim on https://code....
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