EzTag: tagging biomedical concepts via interactive learning. Issue Volume 46:Issue W1(2018) (18th May 2018)

• Record Type: Journal Article
• Authors: Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong
• Is Part Of: Nucleic acids research. Volume 46:Issue W1(2018)
• Page Start: W523
• Page End: W529
• Publication Date: 2018-05-18
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky428
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org .