Contrastive learning enhanced retrieval-augmented few-shot framework for multi-label patent classification.

ABSTRACT: The rapid expansion of patent databases poses increasing challenges for multi-label patent classification, particularly for inventions spanning multiple technological domains. Conventional approaches are hindered by high annotation costs and limited scalability, while often neglecting the semantic structure of patent documents. Here, we present a retrieval-enhanced few-shot learning framework that combines patent-specific contrastive pre-training with semantic retrieval to enable scalable multi-label classification. Drone technologies are selected as the evaluation domain due to their multidisciplinary characteristics encompassing mechanical, electronic, and software aspects. The proposed method learns domain-adapted embeddings that capture multi-label co-occurrence patterns and leverages

SUBMITTER: Zheng W 

PROVIDER: S-EPMC12822942 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature