Journal article

Knowledge based word-concept model estimation and refinement for biomedical text mining

A Jimeno Yepes, R Berlanga

Journal of Biomedical Informatics | Elsevier | Published : 2015


Abstract Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale bio..

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Awarded by CICYT Project from the Spanish Ministry of Economy and Competitiveness (MINECO)

Funding Acknowledgements

We thank anonymous reviewers for their very useful comments and suggestions. Special thanks to Bridget McInnes and Maika Vicente Navarro for proofreading the manuscript. The work was supported by the CICYT Project TIN2011-24147 from the Spanish Ministry of Economy and Competitiveness (MINECO).