What are vector embeddings primarily used to represent?

Vector embeddings are primarily used to represent data points based on meaning and context. This approach stems from the fundamental idea that similar data points should have similar vector representations in a high-dimensional space. In various applications, such as natural language processing, vectors are capable of capturing the semantic relationships between words, sentences, or larger text constructs.

For instance, in word embeddings like Word2Vec or GloVe, words that have similar meanings are located nearby in the vector space. This allows for the performance of various mathematical operations on the vectors, such as finding analogous words or measuring similarity, leveraging their embedded context.

Other options, such as representing physical locations, mathematical operations, or binary classification models, do not inherently reflect the notion of meaning and context that vector embeddings are designed to capture. Therefore, the focus of vector embeddings is specifically on the representation of data points that express semantic meaning and the relational context between them, making this the most appropriate choice.