Embedding, application, and representation
In the previous section, we discussed how to use vectors to represent text. These vectors are digestible for a computer, but they still suffer from some problems (sparsity, high dimensionality, etc.). According to the distributional hypothesis, words with a similar meaning frequently appear close together (or words that appear often in the same context have the same meaning). Similarly, a word can have a different meaning depending on its context: “I went to deposit money in the bank” or “We went to do a picnic on the river bank.” In the following diagram, we have a high-level representation of the embedding process. So, we want a process that allows us to start from text to obtain an array of vectors, where each vector corresponds to the representation of a word. In this case, we want a model that will then allow us to map each word to a vector representation. In the next section, we will describe the process in...
