Skip N-grams and ranking functions for predicting script events

Bram Jans, Steven Bethard, Ivan Vulíc, Marie Francine Moens

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

62 Scopus citations

Abstract

In this paper, we extend current state-of-Theart research on unsupervised acquisition of scripts, that is, stereotypical and frequently observed sequences of events. We design, evaluate and compare different methods for constructing models for script event prediction: given a partial chain of events in a script, predict other events that are likely to belong to the script. Our work aims to answer key questions about how best to (1) identify representative event chains from a source text, (2) gather statistics from the event chains, and (3) choose ranking functions for predicting new script events. We make several contributions, introducing skip-grams for collecting event statistics, designing improved methods for ranking event predictions, defining a more reliable evaluation metric for measuring predictiveness, and providing a systematic analysis of the various event prediction models.

Original languageEnglish (US)
Title of host publicationEACL 2012 - 13th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages336-344
Number of pages9
ISBN (Electronic)9781937284190
StatePublished - 2012
Externally publishedYes
Event13th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2012 - Avignon, France
Duration: Apr 23 2012Apr 27 2012

Publication series

NameEACL 2012 - 13th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings

Conference

Conference13th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2012
Country/TerritoryFrance
CityAvignon
Period4/23/124/27/12

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software

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