用语义相似度改进文献相关性测量，更准地识别同主题论文
Measuring the relatedness between scientific publications using controlled vocabularies
- 引入软余弦和最大词项相似度，考虑非精确匹配词的语义关联
- 在TREC 2006基因组赛道上，软余弦准确率显著优于传统余弦方法
- 适合需要精准文献关联分析的研究者与政策制定者参考
衡量科学文献间的相关性在文献计量学与科技政策中至关重要。受控词汇表为相关性测量提供了良好基础，常与萨尔顿余弦相似度结合使用。然而，该方法仅依赖术语的精确匹配，存在局限。本文提出两种替代方法——软余弦与最大词项相似度，能捕捉非匹配术语间的语义相似性。通过TREC 2006基因组赛道的文献主题分配任务评估三者的准确性，假设同一主题内的文献对应高相关性得分，跨主题则低。结果表明，软余弦方法最为准确，而广泛使用的萨尔顿余弦版本明显逊色。研究结果对如何利用受控词汇表进行相关性测量具有重要启示。
原文摘要 · Abstract (English)
Measuring the relatedness between scientific publications is essential in many areas of bibliometrics and science policy. Controlled vocabularies provide a promising basis for measuring relatedness and are widely used in combination with Salton's cosine similarity. The latter is problematic because it only considers exact matches between terms. This article introduces two alternative methods - soft cosine and maximum term similarities - that account for the semantic similarity between non-matching terms. The article compares the accuracy of all three methods using the assignment of publications to topics in the TREC 2006 Genomics Track and the assumption that accurate relatedness measures should assign high relatedness scores to publication pairs within the same topic and low scores to pairs from separate topics. Results show that soft cosine is the most accurate method, while the most widely used version of Salton's cosine is markedly less accurate than the other methods tested. These findings have implications for how controlled vocabularies should be used to measure relatedness.
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。