Biological knowledgebases rely on expert biocuration of the research literature 
to maintain up-to-date collections of data organized in machine-readable form. 
To enter information into knowledgebases, curators need to follow three steps: 
(i) identify papers containing relevant data, a process called triaging; (ii) 
recognize named entities; and (iii) extract and curate data in accordance with 
the underlying data models. WormBase (WB), the authoritative repository for 
research data on Caenorhabditis elegans and other nematodes, uses text mining 
(TM) to semi-automate its curation pipeline. In addition, WB engages its 
community, via an Author First Pass (AFP) system, to help recognize entities and 
classify data types in their recently published papers. In this paper, we 
present a new WB AFP system that combines TM and AFP into a single application 
to enhance community curation. The system employs string-searching algorithms 
and statistical methods (e.g. support vector machines (SVMs)) to extract 
biological entities and classify data types, and it presents the results to 
authors in a web form where they validate the extracted information, rather than 
enter it de novo as the previous form required. With this new system, we lessen 
the burden for authors, while at the same time receive valuable feedback on the 
performance of our TM tools. The new user interface also links out to specific 
structured data submission forms, e.g. for phenotype or expression pattern data, 
giving the authors the opportunity to contribute a more detailed curation that 
can be incorporated into WB with minimal curator review. Our approach is 
generalizable and could be applied to additional knowledgebases that would like 
to engage their user community in assisting with the curation. In the five 
months succeeding the launch of the new system, the response rate has been 
comparable with that of the previous AFP version, but the quality and quantity 
of the data received has greatly improved.