Document representations are structured formats, numerical encodings, or textual surrogates used by information retrieval and natural language processing systems to capture the semantic content, features, or identity of documents. In computational retrieval systems, these representations transform raw, unstructured text into standardized forms—such as dense or sparse vector embeddings, bag-of-words models, semantic identifiers, or associated synthetic queries—suitable for automated indexing and matching. By encoding the underlying meaning or topical focus of a text into an abstracted surrogate form, machine learning models and search algorithms can efficiently store large corpora, evaluate semantic relevance, and rank documents in response to user queries.
