Category: Natural Language Processing

  • Word Representation and Vectorization

    Introduction to Vectorization We can see that in one-hot encoding, every single word in ourtext corresponds to a vector element.For ex: Here “Queen” is [0,1,0,0,0] and “room” is [0,0,0,0,1] One disadvantage of working with sparse matrices is that they end up consuming a lot of memory, especially as the size of the corpus increases, making…