Excitatory or inhibitory: a new semantic orientation extracts contradiction and causality from the web

Authors:
Chikara Hashimoto;Kentaro Torisawa;Stijn De Saeger;Jong-Hoon Oh;Jun'ichi Kazama
Affiliations:
National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

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Abstract

We propose a new semantic orientation, Excitation, and its automatic acquisition method. Excitation is a semantic property of predicates that classifies them into excitatory, inhibitory and neutral. We show that Excitation is useful for extracting contradiction pairs (e.g., destroy cancer ⊥ develop cancer) and causality pairs (e.g., increase in crime ⇒ heighten anxiety). Our experiments show that with automatically acquired Excitation knowledge we can extract one million contradiction pairs and 500,000 causality pairs with about 70% precision from a 600 million page Web corpus. Furthermore, by combining these extracted causality and contradiction pairs, we can generate one million plausible causality hypotheses that are not written in any single sentence in our corpus with reasonable precision.