Doc2Vec | This repository is tell of Doc2Vec and Word2Vec

Doc2Vec is a Python library. Doc2Vec has no bugs, it has no vulnerabilities and it has low support. However Doc2Vec build file is not available. You can download it from GitHub.

Two models here: cbow ( continuous bag of words) where we use a bag of words to predict a target word and skip-gram where we use one word to predict its neighbors. After this idea is proved to be effective and helpful, say, you can easily cluster and find similar words in a huge corpus, people then began thinking further: is it possible to have a higher level of representation on sentences, paragraphs or even documents. Similarly, there are two models in doc2vec: dbow and dm. dbow (distributed bag of words) It is a simpler model that ignores word order and training stage is quicker. The model uses no-local context/neighboring words in predictions. You see it is not considering the order of the words. From the paper [4], the figure below shows dbow. dm (distributed memory) We treat the paragraph as an extra word. Then it is concatenated/averaged with local context word vectors when making predictions. During training, both paragraph and word embeddings are updated. It calls for more computation and complexity.