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 is mapped to a vector, and the system returns all texts whose vectors lie within a chosen radius according to a chosen distance metric, such as cosine distance, Euclidean distance, or inner product. The radius defines the balance between precision and recall: a smaller radius yields more tightly related results, while a larger radius broadens the set of retrieved items.

Textsranging typically follows a pipeline that includes text preprocessing, embedding generation, index construction, and radius-based querying. Some implementations incorporate re-ranking steps to improve order quality, using additional signals such as keyword overlap, cross-encoder scores, or task-specific objectives.

Advantages of textsranging include efficient handling of large corpora, direct access to semantically similar content, and flexibility in tuning retrieval granularity. Limitations involve dependence on embedding quality, sensitivity to the chosen distance metric and radius, and potential computational overhead for updating the index as data changes.

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.