What role do embeddings play in NLP?

Embeddings are a crucial component in natural language processing as they transform words into numerical vectors. This conversion is essential because machine learning models require numerical input rather than raw text. The vectors generated through embeddings capture both the syntactic and semantic properties of the words, allowing the model to understand relationships and meanings.

For example, embeddings can help to distinguish between words that have similar contexts or meanings, such as “king” and “queen” or “cat” and “dog.” Through these numerical representations, the model can process language more effectively, recognizing patterns and making predictions based on the contextual information embedded within the vectors. This reveals the relationships between different words in a more nuanced way than simple one-hot encoding approaches.

The other options, while related to various aspects of NLP, do not accurately describe the fundamental role of embeddings. Preprocessing text data typically involves techniques such as tokenization or normalization, enhancing graphical representations pertains more to visualization techniques rather than embeddings themselves, and serving as a storage format relates to data management rather than the functional purpose of embeddings in model training and understanding language.