Paper: Skip N-grams and Ranking Functions for Predicting Script Events

ACL ID E12-1034
Title Skip N-grams and Ranking Functions for Predicting Script Events
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

In this paper, we extend current state-of-the- art 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 predic- tion: 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, de- signing improved methods for ranking event predictions, defining a more reliable evalu- ation metric...