TAILIEUCHUNG - Báo cáo khoa học: "Automatic Detection of Nonreferential It in Spoken Multi-Party Dialog"

We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects. | Automatic Detection of Nonreferential It in Spoken Multi-Party Dialog Christoph Muller EML Research gGmbH Villa Bosch SchloB-Wolfsbrunnenweg 33 69118 Heidelberg Germany Abstract We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects. 1 Introduction This paper describes an implemented system for the detection of nonreferential it in spoken multiparty dialog. The system has been developed on the basis of meeting transcriptions from the ICSI Meeting Corpus Janin et al. 2003 and it is intended as a preprocessing component for a coreference resolution system in the DIANA-Summ dialog summarization project. Consider the following utterance MN059 Yeah. Yeah. Yeah. I m sure I could learn a lot about um yeah just how to - how to come up with these structures cuz it s - it s very easy to whip up something quickly but it maybe then makes sense to -to me but not to anybody else and - and if we want to share and integrate things they must - well they must be well designed really. Bed017 In this example only one of the three instances of it is a referential pronoun The first it appears in the reparandum part of a speech repair Heeman Allen 1999 . It is replaced by a subsequent alteration and is thus not part of the final utterance. The second it is the subject of an extraposition construction and serves as the placeholder for the postposed infinitive phrase to whip up something quickly. Only the third it is a referential pronoun which anaphorically refers to something. The task of the system described in the following is to identify and filter out nonreferential instances of .

• Báo cáo khoa học
• Automatic Detection of Nonreferential
• Spoken Multi Party Dialog
• Christoph Muller
• 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