Doc2Vec Document Vectorization and clustering
Introduction In my previous blog posts, I have written about word vectorization with implementation and use cases.You can read about it here . But many times we need to mine the relationships between the phrases rather than the sentences. To take an example John has taken many leaves the year Leaves are falling of the tree In these two sentences, a common word "leaves" has a different meaning based on the sentence in which it is used. This meaning can only be captured when we are taking the context of the complete phrase. Or we would like to measure the similarity of the phrases and cluster them under one name. This is going to more of implementation of the doc2vec in python rather than going into the details of the algorithms. The algorithms use either hierarchical softmax or negative sampling; see Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean: “Efficient Estimation of Word Representations in Vector Space, in Proceedings of W...