TAILIEUCHUNG - Báo cáo khoa học: "Skip N-grams and Ranking Functions for Predicting Script Events"

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. . | Skip N-grams and Ranking Functions for Predicting Script Events Bram Jans Steven Bethard KU Leuven University of Colorado Boulder Leuven Belgium Boulder Colorado USA Ivan VuliC KU Leuven Leuven Belgium 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. 1 Introduction There has been recent interest in automatically acquiring world knowledge in the form of scripts Schank and Abelson 1977 that is frequently recurring situations that have a stereotypical sequence of events such as a visit to a restaurant. All of the techniques so far proposed for this task share a common sub-task given an event or partial chain of events predict other events that belong to the same script Chambers and Jurafsky 2008 Chambers and Jurafsky 2009 Chambers and Ju-rafsky 2011 Manshadi et al. 2008 McIntyre and Lapata 2009 McIntyre and Lapata 2010 Regneri et al. 2010 . Such a model can then serve as input to a system that identifies the order of the events Marie Francine Moens KU Leuven Leuven Belgium within that script Chambers and Jurafsky 2008 Chambers and Jurafsky 2009

• Báo cáo khoa học
• Skip N grams
• Ranking Functions
• Predicting Script Events
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
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• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln