Developing Annotated Resources for Internal Displacement Monitoring
| Autor: | Yunbai Zhang, Fabio Poletto, Sylvain Ponserre, Daniela Paolotti, Yelena Mejova, André Panisson |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Disaster monitoring Computer Science - Computation and Language Computer science Document classification computer.software_genre Displacement (linguistics) Data science Annotation Information extraction Schema (psychology) Benchmark (surveying) Relevance (information retrieval) computer Computation and Language (cs.CL) |
| Zdroj: | WWW (Companion Volume) |
| DOI: | 10.48550/arxiv.2104.05459 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|