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

• URL: http://arxiv.org/abs/2608.16918v1
• Date: Fri, 31 Jul 2026 09:41:42 GMT
• Title: Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval
• Abstract summary: We propose Sparse Coverage, an unsupervised semantic retrieval framework.<n>Sparse Coverage maps local span embeddings to a sparse vocabulary of embedding-space centers.<n> Experiments on CLEF-IP 2013 show that Sparse Coverage matches or exceeds the document-level recall of strong dense patent encoders.
• Score: 15.73941714079915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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