| Literature DB >> 24140287 |
Linna He1, Zhihao Yang2, Hongfei Lin1, Yanpeng Li1.
Abstract
Currently, there is an urgent need to develop a technology for extracting drug information automatically from biomedical texts, and drug name recognition is an essential prerequisite for extracting drug information. This article presents a machine-learning-based approach to recognize drug names in biomedical texts. In this approach, a drug name dictionary is first constructed with the external resource of DrugBank and PubMed. Then a semi-supervised learning method, feature coupling generalization, is used to filter this dictionary. Finally, the dictionary look-up and the condition random field method are combined to recognize drug names. Experimental results show that our approach achieves an F-score of 92.54% on the test set of DDIExtraction2011.Mesh:
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Year: 2013 PMID: 24140287 DOI: 10.1016/j.drudis.2013.10.006
Source DB: PubMed Journal: Drug Discov Today ISSN: 1359-6446 Impact factor: 7.851