Towards Domain Balance Based on Semantic Decomposition for Patent Relevance Assessment

Recent studies increasingly leverage pre-trained language models (PLMs) for patent relevance assessment. In practice, whether a query patent and the candidate patent share the technical domain is a critical factor in relevance assessment. Existing PLM-based rerankers usually ignore domain information, leading to domain-insensitive semantic modeling. In particular, distributional discrepancies between a query and the candidate with different domain labels introduce domain bias. To address these issues, we propose a Domain-Sensitive Semantic Decomposition Network (DSSDNet), including native semantic decomposition, multi-field fusion regression and pairwise ranking with hard negative mining, for patent relevance assessment. It takes a query and the candidate to generate the technical representation which is decomposed into domain-sensitive and domain-insensitive parts via the gating mechanism with three constraints. And a domain-balanced focal loss is designed to remove domain bias existing in the domain-sensitive part. In addition, multi-field fusion regression is introduced to model the overall technical semantics by incorporating both domain-sensitive and domain-insensitive parts, along with domain information. As for pairwise ranking with hard negative mining, it optimizes the re-ranking objective from a holistic ranking perspective through increasing the margin between positive and negative instances. Experiments on the public CLEF-IP 2011 demonstrate that DSSDNet consistently outperforms strong baselines, achieving gains of 2.5–17% in Recall, 2–7% in MAP, and 3–15% in PRES at different cut-off levels. These results indicate that explicitly modeling domain-sensitive and domain-insensitive semantics is an effective way to mitigate domain bias and enhance patent re-ranking performance.