TAILIEUCHUNG - Báo cáo khoa học: "Real Reading Behavior"

words take longer to encode and lexically access), and the text level (more important parts of the text, like topics or definitions take longer to process than less important parts). This analysis generated a verbal description of a model of the reading process that is consistent with the observed fixation durations. The details of the data, analysis, and model are reported elsewhere [5]. Some of the most intriguing aspects of the eye-fixation data concern trends that we have failed to find. Trends within noun phrases and verb phrases seem notable by their absence. . | Real Reading Behavior Robert Thibadeau Marcel Just and Patricia Carpenter Carnegie-Mellon University Pittsburgh PA 15213 Abstract The most obvious observable activities that accompany reading are the eye fixations on various parts of the text. Our laboratory has now developed the technology for automatically measuring and recording the sequence and duration of eye fixations that readers make in a fairly natural reading situation. This paper reports on research in progress to use our observations of this real reading behavior to construct computational models of the cognitive processes involved in natural reading. In the first part of this paper we consider some constraints placed on models of human language comprehension imposed by the eye fixation data. In the second part we propose a particular model whose processing time on each word of the text is proportional to human readers fixation durations. Some Observations The reason that eye fixation data provide a rich base for a theoretical model of language processing is that readers pauses on various words of a text are distinctly non-uniform. Some words are looked at very briefly while others are gazed at for one or two seconds. The longer pauses are associated with a need for more computation 2 . The span of apprehension is relatively small so that at a normal reading distance a reader cannot extract the meaning of words that are in peripheral vision 6 . This means that a person can read only what he looks at and for scientific texts read normally by college students this involves looking at almost every word. Furthermore the longer pauses can occur immediately on the word that triggers the additional computation 4 Thus it is possible to infer the degree of computational load at each point in the text. The starting point for the computer model was the analysis of the eye fixations of 14 Carnegie-Mellon undergraduates reading 15 passages each about 140 words long taken from the science and technology sections of .

• Báo cáo khoa học
• Real Reading Behavior
• Marcel Just
• Long Paper
• 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