Scripts represent knowledge of stereotypical event sequences that can aid text understanding. Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument. We present a script learning approach that employs events with multiple arguments. Unlike previous work, we model the interactions between multiple entities in a script. Experiments on a large corpus using the task of inferring held-out events (the "narrative cloze evaluation") demonstrate that modeling multi-argument events improves predictive accuracy.

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In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), pp. 220--229, Gothenburg, Sweden, April 2014.

@inproceedings{pichotta:eacl14, title={Statistical Script Learning with Multi-Argument Events}, author={Karl Pichotta and Raymond J. Mooney}, booktitle={Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014)}, month={April}, address={Gothenburg, Sweden}, pages={220--229}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127421", year={2014} }

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