stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
- A. McCallum, Wei Li. "Early results for named entity recognition with conditional random fields, feature induction and Web-enhanced lexicons”, Proc. of the 7th CoNLL , Edmonton, Canada: Morgan Kaufmann, 2003, PP. 188-191.
- Hongkui Yu , Huaping Zhang," Recognition of Chinese organization name based on role tagging." Proceedings of 20th In ternational Conference on Computer Processing of Oriental Languages, 2003,PP.79∼87.
- Hongkui Yu, Huaping Zhang, and Quan Liu. "Recognition of Chinese Organization Name based Role Tagging." ,Advances in Computation of Oriental Languages. Beijing: Tsinghua University Press,2003, PP. 79–87.
- Houfeng Wang, Wuguang Shi, "A Simple Rule-Based Approach to Organization Name Recognition in Chinese Text.", 5th CICLing. Heidelberg: Springer-Verlag, 2005, LNCS 3406, PP. 769-772.
- John Lafferty, Andrew McCallum, and Fernando Pereira,"Conditional random fields: Probabilistic models for segmenting and labeling sequence data", Proc. of the 18th ICML . San Francisco: Morgan Kaufmann, 2001. PP.282-289.
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