What term is commonly used to describe the arrangement of similar words close together in vector space?

The term "word embedding" refers to the representation of words in a continuous vector space, where similar words are positioned close to one another. This concept arises from the use of algorithms that convert words into dense vectors, capturing semantic relationships and contextual similarities between words. The key characteristic of word embeddings is that they enable the model to understand and represent the nuances of meaning based on context and usage, allowing for effective natural language processing tasks.

In contrast, while clustering may refer to grouping similar items, it does not specifically denote the arrangement of words within a vector space. Similarly, "word similarity" describes a relationship between words but does not denote their specific placement in a vector framework. The term "vector distance" relates to the measurement of similarity or dissimilarity between vectors in the embedding space but does not designate the overall concept of the arrangement of words. Therefore, "word embedding" is the most accurate term to define the spatial arrangement of similar words in vector space.