Toward future scenario generation: Extracting event causality exploiting semantic relation, context, and association features

Abstract

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture → exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture → exacerbate desertification → increase Asian dust (from China) → asthma gets worse. Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues → global warming worsens → sea temperatures rise → vibrio parahaemolyticus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we "predicted" the future event sequence in a sense. © 2014 Association for Computational Linguistics.