Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval

arXiv:2608.16918v1 Announce Type: cross Abstract: 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 the document-level recall of strong dense patent encoders in several configurations, while remaining competitive for passage-level retrieval. By combining local semantic evidence with sparse inverted-index search, Sparse Coverage provides an effective first-stage retrieval approach for patent search.

Related

• Semantic Recall for Vector Search
• What Limits Does Quantization Place on Dense Top-k Retrieval? A Theoretical Study
• Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching

Source: arXiv cs.AI | 2026-08-19