Pakhomov Serguei, McInnes Bridget, Adam Terrence, Liu Ying, Pedersen Ted, Melton Genevieve B
College of Pharmacy, University of Minnesota, MN, USA.
AMIA Annu Symp Proc. 2010 Nov 13;2010:572-6.
Automated approaches to measuring semantic similarity and relatedness can provide necessary semantic context information for information retrieval applications and a number of fundamental natural language processing tasks including word sense disambiguation. Challenges for the development of these approaches include the limited availability of validated reference standards and the need for better understanding of the notions of semantic relatedness and similarity in medical vocabulary. We present results of a study in which eight medical residents were asked to judge 724 pairs of medical terms for semantic similarity and relatedness. The results of the study confirm the existence of a measurable mental representation of semantic relatedness between medical terms that is distinct from similarity and independent of the context in which the terms occur. This study produced a validated publicly available dataset for developing automated approaches to measuring semantic relatedness and similarity.
用于测量语义相似性和相关性的自动化方法可为信息检索应用以及包括词义消歧在内的一些基本自然语言处理任务提供必要的语义上下文信息。开发这些方法面临的挑战包括经过验证的参考标准的可用性有限,以及需要更好地理解医学词汇中语义相关性和相似性的概念。我们展示了一项研究的结果,在该研究中,八名住院医生被要求判断724对医学术语的语义相似性和相关性。该研究结果证实,医学术语之间存在一种可测量的语义相关性心理表征,它与相似性不同,且独立于术语出现的上下文。这项研究产生了一个经过验证的公开可用数据集,用于开发测量语义相关性和相似性的自动化方法。