Embedding and embedding space are two closely related concepts in natural language processing (NLP). However, there is a subtle difference between the two.
Embedding is the process of representing a discrete symbol, such as a word or subword, as a dense vector of real numbers. This vector representation captures the semantic meaning of the symbol and its relationships to other symbols.
Embedding space is the high-dimensional vector space in which the embedded vectors are located. This space is typically much higher-dimensional than the original space of discrete symbols, allowing for more nuanced and expressive representations of meaning.
The multi-dimensional space where you map these vectors is known as the “embedding space.”
Multi-Dimensional Space:
The embedding space is a multi-dimensional space, typically of a fixed dimensionality, that is defined by the embeddings themselves. Each dimension of this space represents a feature or aspect captured by the embedding. In the case of word embeddings, for example, these dimensions might encode aspects of meaning, usage, syntax, or other linguistic properties.
Mapping of Vectors:
When you convert tokens (like words) into vectors using an embedding process, these vectors are positioned within this multi-dimensional space.
The location of each vector in this space is determined by its values across the various dimensions. Vectors that are close to each other in this space are often similar in some respect (depending on what the dimensions represent).
Understanding the Embedding Space:
The structure of the embedding space can reveal interesting relationships. For example, in word embeddings, you might find that vectors for words with similar meanings are clustered together. The embedding space is not just a storage of vectors but a meaningful representation where distances and directions can have semantic interpretations.
See Also: Tokenization, Embedding, Embedding vs Encoding, Vector Space