Publications / 2020 Proceedings of the 37th ISARC, Kitakyushu, Japan

Deep Learning-based Question Answering System for Proactive Disaster Management

Yohan Kim, Jiu Sohn, Seongdeok Bang and Hyoungkwan Kim
Pages 1322-1326 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7, ISSN 2413-5844)

As climate change increases the frequency and intensity of natural disasters, proactive disaster management is needed to reduce the damage caused by the natural disasters. Existing reports that record the scale, damage, and response of natural disasters can be used as references for proactive disaster management. However, it is labor-intensive and time-consuming to manually find the necessary information from a number of reports. Thus, this study proposes a natural language processing (NLP)-based question answering system (QA system) for proactive disaster management using the existing reports. This study is focused on paragraphs retrieval, which retrieves paragraphs that have a high similarity to a given question based on the word embedding. The National Hurricane Center's Tropical Cyclone Reports are used to evaluate the proposed method.

Keywords: Deep Learning; Disaster Management; Natural Language Processing; Question Answering System