Which of the following best describes the role of embeddings in NLP?

The role of embeddings in natural language processing (NLP) is fundamentally about representing words as continuous vectors in a high-dimensional space. This mapping allows words with similar meanings to be closer together in this vector space, effectively capturing semantic relationships. By transforming textual data into numerical formats through embeddings, NLP models can better understand and process language, as these vectors provide a representation that NLP algorithms can work with.

Embeddings enable the transfer of contextual information and linguistic nuances into a format that algorithms can interpret. Techniques like Word2Vec and GloVe are examples of utilizing embeddings to achieve this transformation, thereby enhancing the models' ability to perform various tasks such as sentiment analysis, translation, and language generation. This is why the statement regarding embeddings mapping words into vector space is the most accurate description of their role in NLP.