Word embedding can use math to represent relations between words such as man and woman, work and worked
Embedding Weights will be learned while training
Embedding lookup finding the corresponding row in the embedding layer
Embedding dimensions is the number of hidden units
Encode each word as an Integer
Embedding matrix is a weight matrix
Embedding layer is a hidden layer
Each row of the learned embedding matrix is a vector representation of the input word
The column of the embedding matrix is the number of stacked hidden units? Usually in the hundreds?
Words in similar context, expected to have similar embeddings, such as I drink water throughout the day, I drink coffee in the morning, I drink tea in the afternoon.
such as water, coffee, and tea
such as morning, throughout the day, afternoon
map a verb A from present to past
map a verb B from present to past
should be the same embedding weights, or vector transformation
Post a Comment