Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often struggle to capture fine-grained scientific concepts in texts due to their reliance on holistic embeddings and limited domain understanding. Recent approaches leverage large language models (LLMs) to extract fine-grained semantic entities and enhance semantic matching, but they typically treat entities as independent fragments, overlooking the multi-faceted nature of scientific concepts. To address this limitation, we propose Pairwise Semantic Matching (PairSem), a framework that represents relevant semantics as entity-aspect pairs, capturing complex, multi-faceted scientific concepts. PairSem is unsupervised, base retriever-agnostic, and plug-and-play, enabling precise and context-aware matching without requiring query-document labels or entity annotations. Extensive experiments on multiple datasets and retrievers demonstrate that PairSem significantly improves retrieval performance, highlighting the importance of modeling multi-aspect semantics in scientific information retrieval.

翻译：科学文献检索是推动知识发现和支撑跨领域研究的关键任务。然而，现有的稠密检索方法由于依赖整体嵌入表示且领域理解有限，往往难以捕捉文本中细粒度的科学概念。近期研究利用大语言模型（LLMs）提取细粒度语义实体以增强语义匹配，但这些方法通常将实体视为独立片段，忽略了科学概念的多面性。为克服这一局限，我们提出成对语义匹配（PairSem）框架，该框架将相关语义表示为“实体-方面”对，以捕捉复杂、多面的科学概念。PairSem是无监督、与基础检索器无关且即插即用的，无需查询-文档标签或实体标注即可实现精准且上下文感知的匹配。在多个数据集和检索器上的大量实验表明，PairSem显著提升了检索性能，凸显了在科学信息检索中对多方面语义进行建模的重要性。