Vector Space Model & TF-IDF (1968) › 1968: Vector Space Model & TF-IDF
History of Language AIAll chapters Vector Space Model & TF-IDF (1968) › 1968: Vector Space Model & TF-IDFOne section · Sections in this chapter1 / 12 ↓
Reading tools Definitions & inline links
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Article links
Make inline references clickable
By the late 1960s, growing library and research collections were straining manual subject headings and Boolean keyword retrieval. Gerard Salton and his colleagues at Cornell approached the problem through representation: if documents and queries could be expressed numerically, a system could rank matches rather than return an unordered set.
The Vector Space Model represented documents and queries as vectors in a high-dimensional space, with one dimension for each vocabulary term. Retrieval could then compare a query vector with document vectors and rank them by geometric similarity. This replaced exact Boolean matching with a graded score.
TF-IDF (term frequency-inverse document frequency) supplied the weights. A term receives more weight when it occurs often in one document but in few documents across the collection. The resulting vectors emphasize terms that distinguish one document from another.
Salton's SMART (System for the Mechanical Analysis and Retrieval of Text) system provided an experimental platform for testing vector-space retrieval. The general idea of representing linguistic objects as vectors also became useful beyond classical document retrieval, though later embeddings learn very different representations from TF-IDF.