%0 Journal Article %J Database %D 2013 %T An overview of the BioCreative 2012 Workshop Track III: interactive text mining task %A Arighi, Cecilia N. %A Carterette, Ben %A Cohen, K. Bretonnel %A Krallinger, Martin %A Wilbur, W. John %A Fey, Petra %A Dodson, Robert %A Cooper, Laurel %A Van Slyke, Ceri E. %A Dahdul, Wasila %A Mabee, Paula %A Li, Donghui %A Harris, Bethany %A Gillespie, Marc %A Jimenez, Silvia %A Roberts, Phoebe %A Matthews, Lisa %A Becker, Kevin %A Drabkin, Harold %A Bello, Susan %A Licata, Luana %A Chatr-aryamontri, Andrew %A Schaeffer, Mary L %A Park, Julie %A Haendel, Melissa %A Van Auken, Kimberly %A Li, Yuling %A Chan, Juancarlos %A Muller, Hans-Michael %A Cui, Hong %A Balhoff, James P. %A Chi-Yang Wu, Johnny %A Lu, Zhiyong %A Wei, Chih-Hsuan %A Tudor, Catalina O. %A Raja, Kalpana %A Subramani, Suresh %A Natarajan, Jeyakumar %A Cejuela, Juan Miguel %A Dubey, Pratibha %A Wu, Cathy %X In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (\~{}1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV. %B Database %V 2013 %8 2013 %G eng %U http://database.oxfordjournals.org/content/2013/bas056.abstract %0 Journal Article %J Database %D 2012 %T Text mining in the biocuration workflow: applications for literature curation at WormBase, dictyBase and TAIR %A Van Auken, Kimberly %A Fey, Petra %A Berardini, Tanya Z %A Dodson, Robert %A Cooper, Laurel %A Li, Donghui %A Chan, Juancarlos %A Li, Yuling %A Basu, Siddhartha %A Muller, Hans-Michael %A Chisholm, Rex %A Huala, Eva %A Sternberg, Paul W. %X WormBase, dictyBase and The Arabidopsis Information Resource (TAIR) are model organism databases containing information about Caenorhabditis elegans and other nematodes, the social amoeba Dictyostelium discoideum and related Dictyostelids and the flowering plant Arabidopsis thaliana, respectively. Each database curates multiple data types from the primary research literature. In this article, we describe the curation workflow at WormBase, with particular emphasis on our use of text-mining tools (BioCreative 2012, Workshop Track II). We then describe the application of a specific component of that workflow, Textpresso for Cellular Component Curation (CCC), to Gene Ontology (GO) curation at dictyBase and TAIR (BioCreative 2012, Workshop Track III). We find that, with organism-specific modifications, Textpresso can be used by dictyBase and TAIR to annotate gene productions to GO’s Cellular Component (CC) ontology. %B Database %V 2012 %8 2012 %G eng %U http://database.oxfordjournals.org/content/2012/bas040.abstract