{'fraction_non_alphanumeric': 0.018504811250925242, 'fraction_numerical': 0.0029607698001480384, 'mean_word_length': 5.438095238095238, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Study in Domain-Independent Information Extraction for Disaster Management

During and after natural disasters, detailed information about their impact is a key for successful relief operations. In the 21st century, such information can be found on the Web, traditionally provided by news agencies and recently through social media by affected people themselves. Manual information acquisition from such texts requires ongoing reading and analyzing, a costly process with very limited scalability. Automatic extraction offers fast information acquisition, but usually requires specifically trained extraction models based on annotated data. Due to changes in the language used, switching domains like from earthquake to flood requires training a new model in many approaches. Retraining in turn demands annotated data for the new domain. In this work, we study the cross-domain robustness of models for extracting casualty numbers from disaster reports. Our models are based on dictionaries, regular expressions, and patterns in dependency graphs. We provide an evaluation on extraction robustness across two disaster types – earthquakes and floods. It shows that applying extra-domain models without retraining gives a relative F1 decrease of solely 9%. This is a fairly small drop compared to previous results for similar complex extraction tasks.