tailieunhanh - Báo cáo khoa học: "Flexible Answer Typing with Discriminative Preference Ranking"

An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What .? and Which .? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost in MRR for both the first appropriate. | Flexible Answer Typing with Discriminative Preference Ranking Christopher Pinchak Dekang Lin Davood Rafiei Department of Computing Science University of Alberta Edmonton Alberta Canada pinchak drafiei @ Google Inc. 1600 Amphitheatre Parkway Mountain View CA USA lindek@ Abstract An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What . and Which . questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall. 1 Introduction Question answering QA systems have received a great deal of attention because they provide both a natural means of querying via questions and because they return short concise answers. These two advantages simplify the task of finding information relevant to a topic of interest. Questions convey more than simply a natural language query an implicit expectation of answer type is provided along with the question words. The discovery and exploitation of this implicit expected type is called answer typing. We introduce an answer typing method that is sufficiently flexible to use a wide variety of features while at the same time providing a high level of performance. Our answer typing method avoids the use of pre-determined classes that are often lacking for unanticipated answer types. Because answer typing is only part of the QA task a flexible answer typing model ensures that answer typing can be easily and usefully incorporated into a .

• Báo cáo khoa học
• Flexible Answer Typing
• Discriminative Preference Ranking
• Christopher Pinchak
• 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