Intuitive Understanding of Word Embeddings: Count Vectors to Word2Vec

Before we start, have a look at the below examples. So what do the above examples have in common? You possible guessed it right – TEXT processing. All the above three scenarios deal with humongous amount of text to perform different range of tasks like clustering in the google search example, classification in the second and Machine Translation in the third. Humans can deal with text format quite intuitively but provided we have millions of documents being generated in a single day, we cannot have humans performing the above the three tasks. It is neither scalable nor effective. So, how do we make computers of today perform clustering, classification etc on a text data since we know that they are generally inefficient at handling and processing strings or texts for any fruitful outputs?