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应用于生物医学文本的文本挖掘技术现状。

Status of text-mining techniques applied to biomedical text.

作者信息

Erhardt Ramón A-A, Schneider Reinhard, Blaschke Christian

机构信息

Bioalma, Ronda de Poniente 4, 28760 Tres Cantos, Madrid, Spain.

出版信息

Drug Discov Today. 2006 Apr;11(7-8):315-25. doi: 10.1016/j.drudis.2006.02.011.

Abstract

Scientific progress is increasingly based on knowledge and information. Knowledge is now recognized as the driver of productivity and economic growth, leading to a new focus on the role of information in the decision-making process. Most scientific knowledge is registered in publications and other unstructured representations that make it difficult to use and to integrate the information with other sources (e.g. biological databases). Making a computer understand human language has proven to be a complex achievement, but there are techniques capable of detecting, distinguishing and extracting a limited number of different classes of facts. In the biomedical field, extracting information has specific problems: complex and ever-changing nomenclature (especially genes and proteins) and the limited representation of domain knowledge.

摘要

科学进步越来越多地基于知识和信息。如今,知识被视为生产力和经济增长的驱动力,这导致人们重新关注信息在决策过程中的作用。大多数科学知识都记录在出版物和其他非结构化表示中,这使得信息难以使用,也难以与其他来源(如生物数据库)进行整合。事实证明,让计算机理解人类语言是一项复杂的成就,但有一些技术能够检测、区分和提取有限数量的不同类别的事实。在生物医学领域,提取信息存在一些特定问题:复杂且不断变化的术语(尤其是基因和蛋白质)以及领域知识的有限表示。

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