Event Causal Relationship Retrieval

Abstract

Analyzing history has numerous benefits, including understanding what the people in the past did for events and what results they obtained and using historical knowledge to the present. Several past studies have analyzed historical events based on the assumption that each event is described in texts. Most of them analyze how similar the words and their categories used in the descriptions are instead of taking care of event-causal relationships. In this study, we propose an algorithm named the Event Causality relationship similarity Measurement (ECM) to measure the similarity between event-causal relationships. The ECM solves a maximum weight matching problem on a bipartite graph, where the weights are the similarities between the event-causal relationships. We evaluated ECM with previous related works and confirmed that the ECM is the best.