TAILIEUCHUNG - Báo cáo khoa học: "From HOPE en I'ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies "

Computational neurolinguistics (CN) is an approach to computational l i n g u i s t i c s which includes neurally-motivated constraints in the design of models of natural language processing. Furthermore, the knowledge representations included in such models must be supported with documented behaviorial e v ~ c e , normal and pathological. This paper w i l l discuss the contribution of CN models to ~the understanding of l i n g u i s t i c "competence" within recent research efforts to adapt HOPE (Gigley 1981; 1982a; 1982b; 1982c; 1983a), an implemented CN. | From HOPE en 1 ESPERANCE On the Role of Computational Neurolinguistics in Cross-Language Studies Helen M. Gigley Department of Computer Science University of New Hampshire Durham NH 03824 ABSTRACT Computational neurolinguistics CN is an approach to computational linguistics which includes neurally-motivated constraints in the design of models of natural language processing. Furthermore the knowledge representations included in such models must be supported with documented behaviorial evidence normal and pathological . This paper will discuss the contribution of CN models to the understanding of linguistic competence within recent research efforts to adapt HOPE Gigley 1981 1982a 1982b 1982c 1983a an implemented CN model for understanding English to 1 ESPERANCE one which understands French. 1. INTRODUCTION Computational Neurolinguistics CN incorporates initial assumptions about language processing that are often indirectly referenced in other computational approaches to language study. These assumptions focus on neural-like computational mechanisms Ballard 1982 Feldman 1981 Gigley 1982a 1982b 1983a McClelland and Rumelhart 1981 which subserve language behavior Lavorel and Gigley 1983 . Furthermore CN approaches to different aspects of language processing include extensive use of behavioral data. Research exists within the CN paradigm along various behaviorally defined dimensions. These are at the level of phonetic speech studies that simulate speech errors Le-cours and Lhermitte 1969 Reggia and Sanjeev 1984 a model of aphasic language production JARGONAUT Lavorel 1982 as well as within lesionable models at a neural network level. These latter models simulate association discrimination and recognition of patterns employing associative network models that have been tuned or have adaptively learned to relate certain discriminations Gordon 1982 Wood 1978 1980 . The research described in this paper was supported by an NIH-CNRS research exchange grant entitled .

• Báo cáo khoa học
• From HOPE en IESPERANCE
• the Role of Computational Neurolinguistics
• Cross Language Studies
• 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