What do embeddings represent in NLP?

Embeddings represent text converted to numerical representations, which is essential in Natural Language Processing (NLP). This process involves mapping words, phrases, or even entire sentences into high-dimensional vectors that capture semantic meanings. By representing text as numerical vectors, machine learning algorithms can process and analyze linguistic data more effectively, allowing for computations that can determine similarity, relationships, and context between different pieces of text.

This transformation is crucial because raw text cannot be directly utilized by algorithms that require numerical input. Instead, embeddings facilitate the understanding of language by allowing models to recognize patterns, perform classifications, and engage in more complex NLP tasks such as sentiment analysis or machine translation. The effectiveness of embeddings lies in their ability to preserve the contextual meaning of words based on their usage in various contexts, which is pivotal for tasks like word similarity and analogy.

In contrast, the other options focus on different modalities like images and audio signals, or they mention words in a non-processed form, which does not align with the specific concept of embeddings in the context of NLP.