Chemical named entities recognition: a review on approaches and applications.

Chemical named entities recognition: a review on approaches and applications.
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DOI:
10.1186/1758-2946-6-17
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发表时间:
2014
影响因子:
8.6
通讯作者:
Salim N
Salim N
中科院分区:
化学2区
文献类型:
--
作者:
Eltyeb S;Salim N

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在所有学科的数字信息的流量迅速增加,导致迫切需要的技术,可以简化使用这种信息。化学文献中有非常丰富的关于化学实体的信息。从科学文献中提取分子及其相关性质和活动,以“文本挖掘”这些提取的数据并确定上下文关系,有助于研究科学家,特别是药物开发科学家。化学文本挖掘中最重要的挑战之一是识别文本中提到的化学实体。本文简要介绍了化学文献挖掘的基本概念、化学文献的文本内容以及化学文献命名的方法。我们勾勒出基于字典,基于规则和机器学习,以及混合化学命名实体识别方法及其应用解决方案。最后,我们展望了这些方法的优点和缺点以及提取的化学实体的类型。
The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to “text mine” these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.
DOI: 10.1097/00008571-200409000-00002
发表时间: 2004-09-01
期刊: PHARMACOGENETICS
影响因子: --
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