What are word embeddings in neural network

This recipe explains what are word embeddings in neural network

Recipe Objective - What are word embeddings in neural network?

Word embeddings is defined as the dense word vectors that is vector associated with a word. Word embeddings are a low-dimensional floating point vectors that is a dense vectors as opposed to the sparse vectors. Word embeddings learns from the data and are 256 dimensional, 512 dimensional, 1,024dimensional etc when dealing with the large vocabularies. Word embeddings pack the large vocabulary into lower dimensions. On other hand, One-hot encoding words generally leads to vectors that are approximately 20,000 dimensional or greater (for ex. capturing a vocabulary of 20,000 token). Also, vectors obtained through one-hot encoding technique are binary, sparse (mostly made of zeros) in nature.

This recipe explains what are word embeddings, explaining its benefits over One-hot encoded vectors and how it can be obtained.

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Explanation of Word embedding.

Word embeddings can be learned using embedding layers. Steps obtaining of word embeddings for usage in neural networks are as follows:- 1. Learning word embeddings jointly with main task or main problem such as document classification, sentiment prediction, emotion classification etc. Following this, Starting with choosing random word vectors and then learning word vectors in the same way as learning the weights of a neural network. 2. Loading into word embeddings model that were precomputed using the different machine-learning problem. These are called pretrained word embeddings. In reality, Word embeddings are meant to map the human language into a geometric space. It finds in application in Natural language processing.

An embedding layer looks like this: Word Index -> Embedding Layer -> Corresponding word vector

In word embedding, an embedding layer takes input a 2D tensor of integers of shape (samples, sequence_length), where each entry represent a sequence of integers. It can embed sequences of variable lengths. All sequences in a batch of word embedding should have the same length as it has to be packed into a single tensor, so sequences that are shorter than others should be padded with zeros and sequences that are longer tha others should be truncated.

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