Editing Information Retrieval (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Query''' — A user's information need expressed as text (keywords, natural language question, or structured expression).
* '''Document''' — A unit of retrievable information: web page, paragraph, PDF, database record.
* '''Relevance''' — The degree to which a retrieved document satisfies the user's information need.
* '''Inverted index''' — A data structure mapping terms to the documents containing them; the backbone of all keyword-based IR.
* '''TF-IDF (Term Frequency-Inverse Document Frequency)''' — A weighting scheme that ranks terms by their frequency in a document (TF) discounted by their commonness across all documents (IDF).
* '''BM25 (Best Match 25)''' — A probabilistic retrieval function that extends TF-IDF with document length normalization; the dominant lexical retrieval algorithm.
* '''Dense retrieval''' — Retrieving documents by computing similarity between dense vector embeddings of query and documents (vs. sparse keyword matching).
* '''Sparse retrieval''' — Retrieval based on term overlap (BM25, TF-IDF); fast and exact but lacks semantic understanding.
* '''Embedding''' — A dense vector representation of text enabling semantic similarity search.
* '''Bi-encoder''' — A retrieval model with separate encoders for query and document; enables fast approximate nearest neighbor search.
* '''Cross-encoder''' — A model that jointly encodes query + document for highly accurate relevance scoring; too slow for retrieval, used for re-ranking.
* '''FAISS''' — Facebook's library for efficient approximate nearest neighbor search in high-dimensional embedding spaces.
* '''Hybrid retrieval''' — Combining dense and sparse retrieval (e.g., BM25 + dense vectors) for better coverage.
* '''Re-ranking''' — Applying a more accurate (but slower) model to re-order an initial set of retrieved candidates.
* '''BEIR (Benchmarking Information Retrieval)''' — A heterogeneous benchmark suite for evaluating zero-shot dense retrieval across diverse domains.
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