TAILIEUCHUNG - Báo cáo khoa học: "Similarity-Based Methods For Word Sense Disambiguation"

We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates. | Similarity-Based Methods For Word Sense Disambiguation Ido Dagan Dept of Mathematics and Computer Science Bar Ilan University Ramat Gan 52900 Israel Lillian Lee Div. of Engineering and Applied Sciences Harvard University Cambridge MA 01238 USA Fernando Pereira AT T Labs - Research 600 Mountain Ave. Murray Hill NJ 07974 USA Abstract We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40 better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates. 1 Introduction The problem of data sparseness affects all statistical methods for natural language processing. Even large training sets tend to misrepresent low-probability events since rare events may not appear in the training corpus at all. We concentrate here on the problem of estimating the probability of unseen word pairs that is pairs that do not occur in the training set. Katz s back-off scheme Katz 1987 widely used in bigram language modeling estimates the probability of an unseen bigram by utilizing unigram estimates. This has the undesirable result of assigning unseen bigrams the same probability if they are made up of unigrams of the same frequency. Class-based methods Brown et al. 1992 Pereira Tishby and Lee 1993 Resnik 1992 cluster words into classes of similar words so that one can base the estimate of a word pair s probability on the averaged cooccurrence probability of the classes to which the two words belong. However a word is therefore modeled by the average behavior of many words which may cause the given word s idiosyncrasies to be ignored. For instance the word red might well act like a generic color word in most cases but it has distinctive .

• Báo cáo khoa học
• Similarity Based Methods
• Word Sense Disambiguation
• Ido Dagan
• 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