Word Embedding for text classification
The embedding layer is a randomly initialized matrix, with the dimension of (number_of_words_in_vocab * embedding_dimension). The embedding_dimension is custom defined dimension, and an hyper-parmeter that we will have to choose.
Here, the embeddings are updated during back-propagation, and are learnt from your task and task-specific corpus.
However, pre-trained embeddings such as word2vec, glove are learnt in an unsupervised manner on huge corpus. Pre-trianed embeddings provides a good initialization for this embedding layer. Thus, you can use the pre-trained embeddings to initialize this embedding layer, and also choose if you want to freeze these emebeddings or update these embeddings during the back-propagation.
I am new in the NLP community and need more light on something.
I saw that Keras has an Embedding layer that is generally used before the LSTM layer. But what algorithm hides behind it? Is it Word2Vec, Glove or something else?
My task is a supervised text classification problem.