Paper: Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

ACL ID P14-1093
Title Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 vibri...