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Word embedding is the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to the vocabulary size.
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Alternatives to doc2vec?
Depending on your target task. If you are to classify documents, then e.g. fastText has it's own approach and there are other classification techniques, not strictly generating embeddings, like LSA / …
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Word vectors to Sentence Vectors
There is doc2vec algorithm which is modification of word2vec - by the same authors, paper: https://arxiv.org/pdf/1405.4053v2.pdf
And it's implemented e.g. in gensim https://radimrehurek.com/gensim/mo …