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.