Semantic Vector Embeddings

Words are transformed into unique vectors that encapsulate their semantic meanings, allowing for a structured representation of language. Similar words, such as "apple" and "orange," are positioned close together in a multidimensional vector space, highlighting their relatedness. This approach enables a deeper understanding of language by capturing the nuances of meaning within a vast vocabulary.