Disability and poverty are linked in a vicious cycle in the developing world. With limited access to healthcare resources, the disabled population often lacks necessary diagnosis, treatment, and medication. They experience stigma and neglect within their communities. With severely limited healthcare and education for disabilities, both the disabled population and the community remain unaware of not only prevention and treatment but also of social realities and the mistake of stigmatization. In Sierra Leone, a number of social, private, and public sector entities have embraced the opportunity to offer informal disability screening and education through a mobile application (app) preinstalled on smartphones. The app development team has since identified relevant disabilities; compiled a database of descriptions, causes, symptoms, treatments, and support resources; and designed a screening tool. The initial version of the app was field tested in Sierra Leone in May 2016, including extensive engagement with health workers and community members. This article reviews the strategic case for the app, its initial design, the results of field testing, and current and future trajectories for the venture.

This paper provides a quick summary of the following topics: enhancements to the PLUM information extraction engine, what we learned from MUC-6 (the Sixth Message Understanding Conference), the results of an experiment on merging templates from two different information extraction engines, a learning technique for named entity recognition, and towards information extraction from speech.