Stokastik

Machine Learning, AI and Programming

Tag: Embeddings

Designing a Contextual Graphical Model for Words

I have been reading about Word Embedding methods that encode words found in text documents into multi-dimensional vectors. The purpose of encoding into¬†vectors is to give "meaning" to words or phrases¬†in a context. Traditional methods of document classification treat each word in isolation or at-most use a N-gram approach i.e. in vector space, the words are represented as one-hot vectors which are sparse and do not convey any meaning whereas […]

Continue Reading →

Understanding Word Vectors and Word2Vec

Quite recently I have been exploring the Word2Vec tool, for representing words in text documents as vectors. I got the initial ideas about word2vec utility from Google's code archive webpage. The idea behind coming up with this kind of utility caught my interest and later I went on to read the following papers by Mikolov et. al. to better understand the algorithm and its implementation. Efficient Estimation of Word Representations […]

Continue Reading →