TAILIEUCHUNG - Báo cáo khoa học: "Adaptivity in Question Answering with User Modelling and a Dialogue Interface"

Most question answering (QA) and information retrieval (IR) systems are insensitive to different users’ needs and preferences, and also to the existence of multiple, complex or controversial answers. We introduce adaptivity in QA and IR by creating a hybrid system based on a dialogue interface and a user model. Keywords: question answering, information retrieval, user modelling, dialogue interfaces. | Adaptivity in Question Answering with User Modelling and a Dialogue Interface Silvia Quarteroni and Suresh Manandhar Department of Computer Science University of York York YO10 5DD UK silvia suresh @ Abstract Most question answering QA and information retrieval IR systems are insensitive to different users needs and preferences and also to the existence of multiple complex or controversial answers. We introduce adaptivity in QA and IR by creating a hybrid system based on a dialogue interface and a user model. Keywords question answering information retrieval user modelling dialogue interfaces. 1 Introduction While standard information retrieval IR systems present the results of a query in the form of a ranked list of relevant documents question answering QA systems attempt to return them in the form of sentences or paragraphs or phrases responding more precisely to the user s request. However in most state-of-the-art QA systems the output remains independent of the questioner s characteristics goals and needs. In other words there is a lack of user modelling a 10-year-old and a University History student would get the same answer to the question When did the Middle Ages begin . Secondly most of the effort of current QA is on factoid questions . questions concerning people dates etc. which can generally be answered by a short sentence or phrase Kwok et al. 2001 . The main QA evaluation campaign TREC-QA 1 has long focused on this type of questions for which the simplifying assumption is that there exists only one correct answer. Even re-centTREC campaigns Voorhees 2003 Voorhees 2004 do not move sufficiently beyond the factoid approach. They account for two types of nonfactoid questions -list and definitional- but not for non-factoid answers. In fact a TREC defines list questions as questions requiring multiple factoid 1 http answers b it is clear that a definition question may be answered by spotting definitional passages what is not .

• Báo cáo khoa học
• Adaptivity in Question Answering
• User Modelling
• Dialogue Interface
• 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