Quantitative Information Extraction from Humanitarian Documents
This work addresses the need for efficient information extraction to support emergency response and anticipatory action in humanitarian efforts, though it is incremental as it builds on existing NLP methods.
The authors tackled the problem of extracting quantitative information from humanitarian documents by creating an annotated dataset and developing a custom NLP pipeline, which achieved consistent performance improvements, especially for documents from the Dominican Republic and select African countries.
Humanitarian action is accompanied by a mass of reports, summaries, news, and other documents. To guide its activities, important information must be quickly extracted from such free-text resources. Quantities, such as the number of people affected, amount of aid distributed, or the extent of infrastructure damage, are central to emergency response and anticipatory action. In this work, we contribute an annotated dataset for the humanitarian domain for the extraction of such quantitative information, along side its important context, including units it refers to, any modifiers, and the relevant event. Further, we develop a custom Natural Language Processing pipeline to extract the quantities alongside their units, and evaluate it in comparison to baseline and recent literature. The proposed model achieves a consistent improvement in the performance, especially in the documents pertaining to the Dominican Republic and select African countries. We make the dataset and code available to the research community to continue the improvement of NLP tools for the humanitarian domain.