用学术分类体系增强论文搜索的语义匹配能力
Taxonomy-guided Semantic Indexing for Academic Paper Search
- 基于学术分类体系构建论文概念索引
- 在少量训练数据下显著提升检索效果
- 提升结果可解释性，适配现有检索系统
学术论文搜索对高效文献发现和科学进步至关重要。尽管密集检索已推动各类即席搜索发展，但其常难以匹配查询与文档间的潜在学术概念，而这正是论文搜索的核心挑战。为此，我们提出分类引导语义索引（TaxoIndex）框架：从论文中提取关键概念，并依据学术分类体系组织为语义索引，以此作为基础认知，识别学术概念并建立查询与文档的关联。该框架为即插即用设计，可灵活增强现有密集检索器。大量实验表明，即使在训练数据极为有限的情况下，TaxoIndex仍带来显著性能提升，并大幅增强结果可解释性。
原文摘要 · Abstract (English)
Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we propose Taxonomy-guided Semantic Indexing (TaxoIndex) framework. TaxoIndex extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy, and then leverages this index as foundational knowledge to identify academic concepts and link queries and documents. As a plug-and-play framework, TaxoIndex can be flexibly employed to enhance existing dense retrievers. Extensive experiments show that TaxoIndex brings significant improvements, even with highly limited training data, and greatly enhances interpretability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。