Abstract

To date, most work on biomedical language processing has addressed entity recognition (e.g., identifying gene names in text), information extraction (finding information about very constrained types of relations between entities, e.g., protein–protein interactions), and information retrieval (e.g., retrieving documents from large text collections), while largely ignoring the important knowledge represented in figures. Literature incorporates an approximation of 100 million figures. An intelligent figure search engine will not only assist biocuration and allow individual biomedical researcher to access figures more efficiently from full-text biomedical articles, but also is an important step towards automatic validations of genome-wide high-throughput predictions. In this talk, I will describe innovative biomedical natural language figure processing (BioNLfP) approaches developed in my lab. BioNLfP semantically associates text with figures, ranks figures based on biological importance, summarizes the content of figures, and evaluates new user-interfaces. BioNLfP is funded by both National Institutes of Health and Elsevier, the latter of which allows BioFigureSearch−the implementation of BioNLfP−to access over 2 million full-text biomedical articles.