A retrieval technique that expands queries or documents with related but not literally present terms. Key feature of learned sparse models like SPLADE, enabling identification of relevant documents even when exact terms don't match.
Loading more......
On this page
Term Expansion
Term Expansion is a retrieval technique that includes alternative but relevant terms beyond those found in the original text. This is what separates modern learned sparse models like SPLADE from traditional keyword search methods like BM25.
BM25 can only match terms that literally appear in both the query and the document. If a user searches for "laptop" but the document only mentions "notebook computer", BM25 will miss it.
Original: "The cat sat on the mat" Expanded: "cat feline kitty sat rested mat rug carpet"
BM25: Only matches exact terms → "laptop" won't match "notebook" SPLADE: Expands terms → "laptop" can match "notebook computer portable"
SPLADE uses:
Too much expansion → noisy results Too little expansion → similar to BM25
SPLADE balances this through:
Various IR evaluation tasks show SPLADE with term expansion achieves significantly better recall compared to BM25, especially for:
Best results combine term expansion (sparse) with dense embeddings:
Available in various vector databases; costs depend on platform.
Explore more items related to this one