Vector space and embedding space are both related concepts in natural language processing (NLP).
Vector space refers to a high-dimensional space where each dimension represents a unique feature or characteristic of the data being represented. For instance, in a vector space representing words, each dimension could represent a specific aspect of word meaning, such as part of speech, sentiment, or semantic similarity.
Embedding space is a specific type of vector space where the vectors represent words, phrases, or other linguistic units. The goal of embedding is to capture the semantic and contextual relationships between these linguistic units within the vector space.
See Also: Vector Database, Word level embedding, Word2vec