Two minutes NLP -- Doc2Vec in a nutshell

Doc2Vec is an unsupervised algorithm that learns embeddings from variable-length pieces of texts, such as sentences, paragraphs, and documents. It's originally presented in the paper Distributed Representations of Sentences and Documents. Let's review Word2Vec first, as it provides the inspiration for the Doc2Vec algorithm. Word2Vec learns word vectors by predicting a word in a sentence using the other words in the context. In this framework, every word is mapped to a unique vector, represented by a column in a matrix W. The concatenation or sum of the vectors is then used as features for the prediction of the next word in a sentence. The word vectors are trained using stochastic gradient descent.