TAILIEUCHUNG - Báo cáo khoa học: "Example-Based Metonymy Recognition for Proper Nouns"

Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity in two ways. First, it will show that the results of the current learning algorithms can be replicated by the ‘lazy’ algorithm of Memory-Based Learning. This approach simply stores all training instances to its memory and classifies a test instance by comparing it to all training examples. Second, this paper will argue that the number of labelled training examples that is currently used in the literature can be reduced drastically. This finding can. | Example-Based Metonymy Recognition for Proper Nouns Yves Peirsman Quantitative Lexicology and Variational Linguistics University of Leuven Belgium Abstract Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity in two ways. First it will show that the results of the current learning algorithms can be replicated by the lazy algorithm of Memory-Based Learning. This approach simply stores all training instances to its memory and classifies a test instance by comparing it to all training examples. Second this paper will argue that the number of labelled training examples that is currently used in the literature can be reduced drastically. This finding can help relieve the knowledge acquisition bottleneck in metonymy recognition and allow the algorithms to be applied on a wider scale. 1 Introduction Metonymy is a figure of speech that uses one entity to refer to another that is related to it Lakoff and Johnson 1980 . In example 1 for instance China and Taiwan stand for the governments of the respective countries 1 China has always threatened to use force if Taiwan declared independence. BNC Metonymy resolution is the task of automatically recognizing these words and determining their referent. It is therefore generally split up into two phases metonymy recognition and metonymy interpretation Fass 1997 . The earliest approaches to metonymy recognition identify a word as metonymical when it violates selectional restrictions Pustejovsky 1995 . Indeed in example 1 China and Taiwan both violate the restriction that threaten and declare require an animate subject and thus have to be interpreted metonymically. However it is clear that many metonymies escape this characterization. Nixon in example 2 does not violate the se-lectional restrictions of the verb to bomb and yet it metonymically refers to the army under Nixon

• Báo cáo khoa học
• Example Based Metonymy Recognition
• Proper Nouns
• Yves Peirsman
• 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