In this post, we are going to talk about word embedding (or word vector), which is how we represent words in NLP. Word embedding is used in many higher-level applications such as sentiment analysis, Q&A, etc. Let's have a look at the most currently widely used models. One-hot vector is a vector of size V, with V is the vocabulary size. It has value 1 in one position (represents the value of this word "appears") and 0 in all other positions. [0, 0, ... 1, .., 0] This is usually used as the input of a word2vec model. It is just operating as a lookup table. So this one-hot encoding treats words as independent units. In fact, we want to find the "similarity" between words for many other higher-level tasks such as document classification, Q&A, etc. The idea is: To capture the meaning of a word, we look at the words that frequently appear close-by this word. Let's have a look at some state-of-the-art architectures that give us the results of word ve...
what does 'to buy in 45%-50%' mean?
ReplyDelete