A novel re-ranking architecture for patent search

Abstract

Patent search presents unique challenges due to the intricate structure and specialized terminology embedded in patent documents. While neural models have been successfully applied in various information retrieval (IR) tasks, these inherent complexities have hindered their effectiveness in patent search. To address these challenges, we propose a novel re-ranking architecture that effectively handles long, structured patent documents and leverages AI models to interpolate lexical and semantic signals of relevance. Additionally, the architecture incorporates query-specific weights for the final re-ranking process. To address partial relevance between patent sections our method effectively models the relevance relationships between different sections of patent documents. We calculate lexical and semantic signals of relevance from each document section and feed them as input features to AI models that estimate a combined relevance score. Finally, we compute query-specific weights to determine the relative contributions of lexical and semantic relevance for the final re-ranking. Extensive experiments on the CLEF-IP dataset demonstrate that our method outperforms several baselines, achieving substantial and statistically significant improvements in retrieval performance. We further assess the adaptability of our method using the MSMARCO dataset, where it exhibits limited performance, indicating its suitability for domain-specific patent research.