A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge

Lea Frermann, Ivan Titov, Manfred Pinkal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Scripts representing common sense knowledge about stereotyped sequences of events have been shown to be a valuable resource for NLP applications. We present a hierarchical Bayesian model for unsupervised learning of script knowledge from crowdsourced descriptions of human activities. Events and constraints on event ordering are induced jointly in one unified framework. We use a statistical model over permutations which captures event ordering constraints in a more flexible way than previous approaches. In order to alleviate the sparsity problem caused by using relatively small datasets, we incorporate in our hierarchical model an informed prior on word distributions. The resulting model substantially outperforms a state-of-the-art method on the event ordering task.
Original languageEnglish
Title of host publicationProceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26-30, 2014, Gothenburg, Sweden
PublisherAssociation for Computational Linguistics
Number of pages9
ISBN (Print)978-1-937284-78-7
Publication statusPublished - 2014


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