TAILIEUCHUNG - Báo cáo khoa học: "Selecting the “Right” Number of Senses Based on Clustering Criterion Functions"

This paper describes an unsupervised knowledge–lean methodology for automatically determining the number of senses in which an ambiguous word is used in a large corpus. It is based on the use of global criterion functions that assess the quality of a clustering solution. | Selecting the Right Number of Senses Based on Clustering Criterion Functions Ted Pedersen and Anagha Kulkarni Department of Computer Science University of Minnesota Duluth Duluth MN 55812 USA tpederse kulka020 @ http Abstract This paper describes an unsupervised knowledge-lean methodology for automatically determining the number of senses in which an ambiguous word is used in a large corpus. It is based on the use of global criterion functions that assess the quality of a clustering solution. 1 Introduction The goal of word sense discrimination is to cluster the occurrences of a word in context based on its underlying meaning. This is often approached as a problem in unsupervised learning where the only information available is a large corpus of text . Pedersen and Bruce 1997 Schutze 1998 Pu-randare and Pedersen 2004 . These methods usually require that the number of clusters to be discovered k be specified ahead of time. However in most realistic settings the value of k is unknown to the user. Word sense discrimination seeks to cluster N contexts each of which contain a particular target word into k clusters where we would like the value of k to be automatically selected. Each context consists of approximately a paragraph of surrounding text where the word to be discriminated the target word is found approximately in the middle of the context. We present a methodology that automatically selects an appropriate value for k. Our strategy is to perform clustering for successive values of k and evaluate the resulting solutions with a criterion function. We select the value of k that is immediately prior to the point at which clustering does not improve significantly. Clustering methods are typically either parti-tional or agglomerative. The main difference is that agglomerative methods start with 1 or N clusters and then iteratively arrive at a pre-specified number k of clusters while partitional methods start by randomly .

• Báo cáo khoa học
• Selecting the Right Number of Senses
• Clustering Criterion Functions
• Ted Pedersen and Anagha Kulkarni
• 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