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As we know Word2Vec is a non-contextual embedding, here it maps the words in global vocabulary and returns their corresponding vectors (at word level).

In case of Doc2Vec, hope this is also non-contextual embedding and it return the vectors at document level, that means internally document is a union of paragraphs, sentences (i.e words).

what is the implementation style of Doc2Vec?

is any difference between Doc2vec, sent2vec,word2vec ? (because for all these word/subword is basic)?

please share the more insights about them?

tovijayak
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    Does this answer your question? [Word2Vec vs. Doc2Vec Word Vectors](https://datascience.stackexchange.com/questions/88834/word2vec-vs-doc2vec-word-vectors). And this? [Doc2Vec or Word2vec for word embedding](https://datascience.stackexchange.com/q/18087) – noe Jun 19 '23 at 06:59
  • Does this answer your question? [Doc2Vec or Word2vec for word embedding](https://datascience.stackexchange.com/questions/18087/doc2vec-or-word2vec-for-word-embedding) – Lynn Jun 26 '23 at 22:56
  • upto some level. in Doc2Vec, each document represent by a dense vector. that means internally, is it concatenation of word embeddings or how it will generate vectors for document? what is the architecture /approach @Lynn – tovijayak Jun 27 '23 at 01:20

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