Abstract
We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 67% of connectivity statements at 51% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/.
Highlights
Neuroinformatics research thrives on plentiful amounts of open and computable neuroscience datasets
We focused on abstracts from one journal, the Journal of Comparative Neurology (JCN), because it is enriched with neuroanatomical studies
User input is restricted to Neurolex brain regions that appear in the corpus
Summary
Neuroinformatics research thrives on plentiful amounts of open and computable neuroscience datasets. This type of data is lacking at the level of brain regions, when compared to molecular data about genes or proteins. Manual curation can join and formalize the findings (Bota et al, 2012, 2014). To speed up this process in the domain of anatomical connectivity, we created the WhiteText project to automatically extract this information from text. WhiteText was designed to extract mentions of brain regions and statements describing connections between them. How accurately can neuroanatomical connectivity information be automatically extracted from neuroscience literature? While developing WhiteText we asked the following questions: 1. How accurately can neuroanatomical connectivity information be automatically extracted from neuroscience literature?
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
