Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features
Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer, Motoki Sano, István Varga, Jong–Hoon Oh, Yutaka Kidawara · 2014
We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture→exacerbate desertification from the web using se-mantic relation (between nouns), context, and association features. Experiments show that our method outperforms base-lines 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. Experi-ments show that we can generate 50,000 scenarios with 68 % precision. We also generated a scenario deforestation con-tinues→global warming worsens→sea temperatures rise→vibrio parahaemolyti-cus 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. 1