Year: 2012
Type: conference-paper
Abstract: 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 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.
Source: Lirias
Institutions: KU Leuven, University of Colorado Boulder
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W1702669762