textsranging
Textsranging is a term used in information retrieval and text mining to describe a class of techniques that locate text items by performing range queries in a semantic embedding space. In textsranging, every document, sentence, or passage is converted into a vector using an embedding model such as a transformer-based encoder. A range index, which may be based on approximate nearest neighbor data structures, kd-trees, ball trees, or locality-sensitive hashing, is built over these vectors to support queries that retrieve items within a specified distance from a query vector.
The fundamental idea is that semantic similarity corresponds to proximity in the embedding space. A query text
Textsranging typically follows a pipeline that includes text preprocessing, embedding generation, index construction, and radius-based querying.
Advantages of textsranging include efficient handling of large corpora, direct access to semantically similar content, and
Applications span search engines, document clustering, content recommendation, paraphrase or duplicate detection, and semantic question-answering systems.
See also: vector search, semantic search, approximate nearest neighbor search, embeddings.