Generating Event Causality Hypotheses through Semantic Relations

Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer, Jong-Hoon Oh

Abstract: Event causality knowledge is indispensable for intelligent natural language understanding. The problem is that any method for extracting event causalities from text is insufficient; it is likely that some event causalities that we can recognize in this world are not written in a corpus, no matter its size. We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns. For example, our method can hypothesize deploy a security camera→avoid crimes from deploy a mosquito net→avoid malaria through semantic relation A PREVENTS B. Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. We also show that our method outperforms a state-of-the-art hypothesis generation method.