We all are aware of (not so) recent advancements in word representation, such as Word2Vec, GloVe etc. for various NLP tasks. Let's try to dig a little deeper of how they work, and why they are so helpful! The basics, what is a Word vector? We need a mathematical way of representing words so as to process them. We call this representation, a word vector. This representation can be as simple as a one-hot encoded vector having the size of the vocabulary. For ex, if we had 3 words in our vocabulary {man, woman, child}, we can generate word vectors in the following manner Man : {0, 0, 1} Woman : {0, 1, 0} Child : {1, 0, 0} Such an encoding cannot be used to for any meaningful comparisons, other than checking for equality. In vectors such as Word2Vec, a word is represented as a distribution over some dimensions. Each word is assigned some particular weight for each of the dimensions. Picking up the previous example, this time the vectors can be as following (assuming a 2 dime