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
中科院分区:
文献类型:
--
作者:
Eltyeb S;Salim N
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.
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DOI:
10.1097/00008571-200409000-00002
发表时间:
2004-09-01
期刊:
PHARMACOGENETICS
影响因子:
--
作者:
Chang, JT;Altman, RB
通讯作者:
Altman, RB
DOI:
10.1021/ci990062c
发表时间:
1999-11-01
期刊:
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子:
--
作者:
Brecher, J
通讯作者:
Brecher, J
影响因子:
3.7
作者:
Davis AP;Wiegers TC;Johnson RJ;Lay JM;Lennon-Hopkins K;Saraceni-Richards C;Sciaky D;Murphy CG;Mattingly CJ
通讯作者:
Mattingly CJ
DOI:
10.1007/978-3-642-34399-5_3
发表时间:
2013-01-01
期刊:
COMPUTATIONAL LINGUISTICS: APPLICATIONS
影响因子:
--
作者:
Broda, Bartosz;Kedzia, Pawel;Wardynski, Adam
通讯作者:
Wardynski, Adam
影响因子:
5.8
作者:
Fundel, Katrin;Kueffner, Robert;Zimmer, Ralf
通讯作者:
Zimmer, Ralf