Unsupervised Discovery of Event Scenarios from Texts

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We propose a new unsupervised learning approach for discovering event scenarios from texts. We interpret an event scenario as a collection of related events that characterize a specific situation. The approach uses the Latent Dirichlet Allocation (LDA) probabilistic model to automatically learn the probability distribution of events corresponding to event scenarios. We performed experiments on an event annotated corpus and compared the automatically extracted event scenarios with frame scenarios defined in FrameNet. The results show a better coverage for those event scenarios that are described in more detail in the event annotated corpus. When compared with a smoothed unigram model, the event scenario model achieves a perplexity reduction of 93.46%.