What is the role of embeddings in neural networks, particularly in natural language processing?

Embeddings are low-dimensional, dense vector representations of words or entities that capture semantic relationships in a continuous vector space. In neural networks, particularly in natural language processing (NLP), embeddings translate discrete categorical data (like words) into a form that can be processed mathematically. This enables the model to learn patterns and meanings from textual data more effectively. Unlike one-hot encoding, embeddings allow for the encoding of similarities between words, meaning that words with similar meanings will have similar embeddings. Popular algorithms for generating embeddings include Word2Vec and GloVe.