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EquatorNLP : Pattern-based Information Extraction for Disaster Response

One of the most severe problems in early phases of disaster response is the lack of information about the current situation. Such information is indispensable for planning and monitoring rescue operations, but hardly available due to the breakdown of information channels and normal message routes. However, during recent disasters in developed countries, such as the flooding of New Orleans or the earthquake in New Zealand, a wealth of detailed information was posted by affected persons in media, such as Flickr, Twitter, or personal blogs. Finding and extracting such information may provide valuable clues for organizing aid, but currently requires humans to constantly read and analyze these messages. In this work, we report on a study for extracting such facts automatically by using a combination of deep natural language processing and advanced machine learning. Specially, we present an approach that learns patterns in dependency representations of sentences to find textually described facts about human fatalities. Our method achieves a F1 measure of 66.7% on a manually annotated corpus of 109 news articles about earthquake effects, demonstrating the general efficacy of our approach.