Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval
Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic matching, but single-vector representations may compress multiple technical components, functions, and constraints into a single embedding. We propose Sparse Coverage, an unsupervised semantic retrieval framework that maps local span embeddings to a sparse vocabulary of embedding-space centers. The centers are selected with a coverage-oriented k-center objective, and spans activate nearby centers to produce sparse representations compatible with inverted-index retrieval. Experiments on CLEF-IP 2013 show that Sparse Coverage matches or exceeds t...
| Authors | You Zuo, Kim Gerdes, Éric de la Clergerie et al. |
|---|---|
| Primary category | cs.IR |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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