Developing Annotated Resources for Internal Displacement Monitoring

TL;DR

This paper presents a new annotation framework and resources for internal displacement data, enhancing monitoring accuracy through detailed event schemas and machine learning case studies.

Contribution

It introduces a comprehensive annotation schema for internal displacement events and demonstrates its application in machine learning classification tasks.

Findings

Improved event annotation schema for internal displacement.

Successful application of machine learning to classify displacement documents.

Emphasizes the importance of standardized schemas for reliable disaster monitoring.

Abstract

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.