Measures of semantic similarity and relatedness in the biomedical domain

Measures of semantic similarity and relatedness in the biomedical domain
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DOI:
10.1016/j.jbi.2006.06.004
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发表时间:
2007-06-01
影响因子:
4.5
通讯作者:
Chute, Christopher G.
Chute, Christopher G.
中科院分区:
医学3区
文献类型:
--
作者:
Pedersen, Ted;Pakhomov, Serguei V. S.;Chute, Christopher G.

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概念之间的语义相似性度量在自然语言处理中被广泛使用。在这篇文章中,我们将展示如何现有的六个独立领域的措施,可以适应生物医学领域。这些措施最初是基于WordNet,英语词汇数据库的概念和关系。在这项研究中,我们适应这些措施的SNOMED-CT(R)的医学概念本体。这些措施包括两个路径为基础的措施,和三个措施,增强路径为基础的措施,从语料库的信息内容统计。我们还推导出一个上下文向量的测量医学语料库的基础上,可以作为一个衡量语义相关性。这六个措施进行评估,对一个新创建的测试床的30个医学概念对三名医生和九个医疗编码器得分。我们发现,医疗编码器和医生在他们的评级不同,和上下文向量测量相关最密切的医生,而基于路径的措施和信息内容的措施之一相关最密切的医疗编码器。我们的结论是,有一个更灵活的措施,相关性的基础上,来自语料库的信息,以及依赖于现有的本体结构的措施的作用。(C)2006年爱思唯尔公司All rights reserved.
Measures of semantic similarity between concepts are widely used in Natural Language Processing. In this article, we show how six existing domain-independent measures can be adapted to the biomedical domain. These measures were originally based on WordNet, an English lexical database of concepts and relations. In this research, we adapt these measures to the SNOMED-CT (R) ontology of medical concepts. The measures include two path-based measures, and three measures that augment path-based measures with information content statistics from corpora. We also derive a context vector measure based on medical corpora that can be used as a measure of semantic relatedness. These six measures are evaluated against a newly created test bed of 30 medical concept pairs scored by three physicians and nine medical coders. We find that the medical coders and physicians differ in their ratings, and that the context vector measure correlates most closely with the physicians, while the path-based measures and one of the information content measures correlates most closely with the medical coders. We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures. (C) 2006 Elsevier Inc. All rights reserved.