A recent advance in the automatic indexing of the biomedical literature.

A recent advance in the automatic indexing of the biomedical literature.
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DOI:
10.1016/j.jbi.2008.12.007
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发表时间:
2009-10
影响因子:
4.5
通讯作者:
Aronson AR
Aronson AR
中科院分区:
医学3区
文献类型:
--
作者:
Névéol A;Shooshan SE;Humphrey SM;Mork JG;Aronson AR

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近年来,生物医学文献的数量经历了爆炸式增长。这反映在MEDLINE®(最大的生物医学引文书目数据库)的规模相应增加。美国国家医学图书馆(NLM)的索引员需要高效的工具来帮助他们适应随之而来的工作量。在回顾了将医学主题词(MeSH®术语)自动分配给生物医学文本的问题后,我们更具体地关注NLM的医学文本索引器(MTI)的新副标题附件功能。自然语言处理,统计和机器学习方法产生自动MeSH主标题/副标题对建议进行了独立评估和组合。最佳组合达到48%的准确率和30%的召回率。经过NLM索引器的验证,本文提出的方法的适当组合被集成到MTI中作为副标题附件功能,产生符合当前最先进的索引实践的MeSH索引建议。
The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE®, the largest bibliographic database of biomedical citations. Indexers at the U.S. National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH® terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM’s Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.
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