What method in natural language processing maps words or phrases to high-dimensional vectors based on their similarity?

The method in natural language processing that maps words or phrases to high-dimensional vectors based on their similarity is best described as word embedding. Word embedding techniques, such as Word2Vec, GloVe, or FastText, place semantically similar words closer together in the vector space, effectively capturing contextual meaning. This mapping is foundational for various NLP tasks since it allows algorithms to process and understand the nuances of human language in a quantitative form.

While vectorization can refer to the general process of converting data into a numerical format that can be more readily analyzed by machine learning algorithms, it often is a broader term that encompasses various techniques. Word embedding is a specific technique within that broader scope, focused on language representation using high-dimensional vectors. Therefore, while both vectorization and word embedding are related concepts, the most accurate and specific answer to the question of mapping words or phrases to their similarity is indeed encapsulated by word embedding. This highlights the importance of the specialized application of embedding techniques within the larger context of vectorization.