TAILIEUCHUNG - Báo cáo khoa học: "Addressee Identification in Face-to-Face Meetings"

We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers’ performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker’s gaze information. The classifiers show little gain from information about meeting context. . | Addressee Identification in Face-to-Face Meetings Natasa Jovanovic Rieks op den Akker and Anton Nijholt University of Twente PO Box 217 Enschede The Netherlands natasa infrieks @ Abstract We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First we investigate how well the addressee of a dialogue act can be predicted based on gaze utterance and conversational context features. Then we explore whether information about meeting context can aid classifiers performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker s gaze information. The classifiers show little gain from information about meeting context. 1 Introduction Addressing is an aspect of every form of communication. It represents a form of orientation and directionality of the act the current actor performs toward the particular other s who are involved in an interaction. In conversational communication involving two participants the hearer is always the addressee of the speech act that the speaker performs. Addressing however becomes a real issue in multi-party conversation. The concept of addressee as well as a variety of mechanisms that people use in addressing their speech have been extensively investigated by conversational analysts and social psychologists Goffman 1981a Goodwin 1981 Clark and Carlson 1982 . Recently addressing has received considerable attention in modeling multi-party interaction in various domains. Research on automatic addressee identification has been conducted in the context of mixed human-human and human-computer interaction Bakx et al. 2003 van Turnhout et al. 2005 human-humanrobot interaction Katzenmaier et al. 2004 and mixed human-agents and multi-agents interaction Traum 2004 . In the context of automatic analysis of multi-party face-to-face conversation Ot-suka et al. 2005 proposed a framework for .

• Báo cáo khoa học
• Addressee Identification
• Face to Face Meetings
• Natasa Jovanovic
• 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
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln