  This paper describes in details the design and development of a novel
annotation framework and of annotated resources for Internal Displacement, as
the outcome of a collaboration with the Internal Displacement Monitoring
Centre, aimed at improving the accuracy of their monitoring platform IDETECT.
The schema includes multi-faceted description of the events, including cause,
quantity of people displaced, location and date. Higher-order facets aimed at
improving the information extraction, such as document relevance and type, are
proposed. We also report a case study of machine learning application to the
document classification tasks. Finally, we discuss the importance of
standardized schema in dataset benchmark development and its impact on the
development of reliable disaster monitoring infrastructure.
