TAILIEUCHUNG - Báo cáo khoa học: "Connectionist Models for Natural Language Processing"

There have been three main reasons for this increase in interest: 1. Scientific adequacy of the models 2. The availability of fine-grained parallel hardware to run the models 3. The demonstration of powerful connectionist learning models. The scientific adequacy of models based on a small number of coarse-grained primitives (. conceptual dependency), popular in AI during the 70's, has been called into question and substantially replaced by a current emphasis in much of computational linguistics on lexicalist models (., ones which use words for representing concepts or meanings). However, few people can doubt that words are too coarse, that. | FORUM ON CONNECTIONISM Connectionist Models for Natural Language Processing David L. Waltz Thinking Machines Corporation 245 First Street Cambridge MA 02142 and Program in Linguistics and Cognitive Science Brandeis University Brown 125 Waltham MA 02254 PANELIST STATEMENT After an almost twenty year lull there has been a dramatic upsurge of interest in massively parallel models for computation descendants of perceptron and pandemonium models now dubbed connectionist models. Much of the connectionist research has focused on models for natural language processing. There have been three main reasons for this increase in interest 1. Scientific adequacy of the models 2. The availability of fine-grained parallel hardware to run the models 3. The demonstration of powerful connectionist learning models. The scientific adequacy of models based on a small number of coarse-grained primitives . conceptual dependency popular in Al during the 70 s has been called into question and substantially replaced by a current emphasis in much of computational linguistics on lexicalist models . ones which use words for representing concepts or meanings . However few people can doubt that words are too coarse that they have structure and properties and features. Connectionist models offer very fine granularity they can capture such detail in a manner that still allows for tractable computation. Such models also promise to make the integration of syntactic semantic pragmatic and memory models simpler and more transparent. Fine-grained hardware such as the Connection Machine can allow models with millions of active elements full vocabularies and rapid throughput as well as powerful nearterm connectionist applications based on the use of associative memory and hardware support for interprocessor communication. Meanwhile connectionist learning models such as the Boltzmann Machine and its descendant the backward error propagation model have demonstrated surprising power in learning concepts

• Báo cáo khoa học
• Connectionist Models
• Natural Language Processing
• David L
• Waltz
• 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
• Mark H
• Bursteln