Creating a Disaster Chain Diagram from Japanese Newspaper Articles Using Mechanical Methods

Abstract

A new method for creating a chain diagram of events that occur during disasters by extracting causal knowledge from Japanese newspaper articles and designing a causal network is proposed herein. Machine learning discriminant models were created for both conventional cue phrases and succession expressions with causation to extract causal sentences. We found that causal sentences can be extracted with a certain degree of accuracy from disaster articles. We were also able to create a causal network using sentences as nodes and links. The chain diagram using our new method extracted events and causal knowledge that were unavailable in a disaster chain diagram designed using conventional methods.