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This paper describes an ecosystem consisting of three independent text annotation platforms. To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognition. The platforms and the approach are in general domain-independent and can be readily applied to other areas of science. 

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). 

In this paper we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annota- tion of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seam- less communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We con- sider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process. 

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. 

The LAPPS-CLARIN project is creating a “trust network” between the Language Applications (LAPPS) Grid and the WebLicht workflow engine hosted by the CLARIN-D Center in T¨ubingen. The project also includes integration of NLP services available from the LINDAT/CLARIN Center in Prague. The goal is to allow users on one side of the bridge to gain appropriately authenticated access to the other and enable seamless communication among tools and resources in both frameworks. The resulting “meta-framework” provides users across the globe with access to an unprecedented array of language processing facilities that cover multiple languages, tasks, and applications, all of which are fully interoperable.