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2 votes
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Is there a sensible notion of 'character embeddings'?

Yes, absolutely. First it's important to understand that word embeddings accurately represent the semantics of the word because they are trained on the context of the word, i.e the words close to the ...
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Why is Word2vec regarded as a neural embedding?

I think you are confused - the reason why Word2Vec is regarded as 'neural' is not due to its loss function, but that it uses neural network to estimate the word embedding ($\vec{u}$ and $\vec{v}$) (...
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4 votes
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Sum vs mean of word-embeddings for sentence similarity

TL;DR You are better off averaging the vectors. Average vs sum Averaging the word vectors is a pretty known approach to get sentence level vectors. Some people may even call that "Sentence2Vec&...
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1 vote
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Why does averaging word embedding vectors (exctracted from the NN embedding layer) work to represent sentences?

A simple, intuitive explanation- think of each latent dimension as a measure of some (very abstract) quality or property of a word. The value a word's coordinate has in that dimension describes how ...
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0 votes

Cluster words into groups of similar meaning (synonyms)

It is not possible to find synonyms by starting with word embeddings. Word embeddings group words by co-occurrence. For example "and" and "but" will be near each other in embedding ...
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