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It is widely recognized that the ability to exploit Natural Language Processing (NLP) text mining strategies has the potential to increase productivity and innovation in the sciences by orders of magnitude, by enabling scientists to pull information from research articles in scientific disciplines such as genomics and biomedicine. These methods enable scientists to rapidly identify publications relevant to their own research as well as make scientific discoveries by scouring hundreds of research papers for associations and connections (such as between drugs and side effects, or genes and disease pathways) that humans reading each paper individually might not notice. The goal of our work is to enable rapid development of workflows for mining scientific publications and, crucially, means to adapt tools and workflows to data for specific disciplines (domain adaptation). Our work on this project is still ongoing; however, as a starting point we initially addressed the needs for mining biomedical publications, and a Galaxy instance of the LAPPS Grid tailored to mining biomedical publications is currently maintained on the JetStream cloud environment (https://jetstream.lappsgrid.org). 

It is widely recognized that the ability to exploit Natural Language Processing (NLP) text mining strategies has the potential to increase productivity and innovation in the sciences by orders of magnitude, by enabling scientists to pull information from research articles in scientific disciplines such as genomics and biomedicine. The Language Applications (LAPPS) Grid is an infrastructure for rapid development of natural language processing applications (NLP) that provides an ideal platform to support mining scientific literature. Its Galaxy interface and the interoperability among tools together provide an intuitive and easy-to-use platform, and users can experiment with and exploit NLP tools and resources without the need to determine which are suited to a particular task, and without the need for significant computer expertise. The LAPPS Grid has collaborated with the developers of PubAnnotation to integrate the services and resources provided by each in order to greatly enhance the user’s ability to annotate scientific publications and share the results. This poster/demo shows how the LAPPS Grid can facilitate mining scientific publications, including identification and extraction of relevant entities, relations, and events; iterative manual correction and evaluation of automatically-produced annotations, and customization of supporting resources to accommodate specific domains.