What do dense, continuous word embeddings like word2vec and GloVe utilize?

Dense, continuous word embeddings like word2vec and GloVe utilize dense representations, which means they represent words as vectors in a continuous vector space. Unlike traditional sparse embeddings, where each word is represented by high-dimensional vectors primarily filled with zeros (such as one-hot encodings), dense representations allow for a more compact and informative representation of words.

These dense vectors capture semantic relationships between words, which enables various natural language processing tasks such as similarity measurement, analogy reasoning, and more. The use of dense representations allows models to efficiently learn and generalize across the variations and nuances in language, as similar words tend to be located closer together in the vector space. The continuous nature of these embeddings is also beneficial for various machine learning algorithms, which often perform better with dense, low-dimensional data compared to sparse representations.

Dense representations enhance the model's ability to understand context, meaning, and usage of words within a corpus, thus significantly improving performance on the tasks that require comprehension of linguistic features.