Trajectories through Knowledge Space
A Dynamic Framework for Machine Comprehension
Published on 30. September 1994
Book
Hardback
XXI, 271 pages
978-0-7923-9487-7 (ISBN)
Description
As any history student will tell you, all events must be understood within their political and sociological context. Yet science provides an interesting counterpoint to this idea, since scientific ideas stand on their own merit, and require no reference to the time and place of their conception beyond perhaps a simple citation. Even so, the historical context of a scientific discovery casts a special light on that discovery - a light that motivates the work and explains its significance against a backdrop of related ideas. The book that you hold in your hands is unusually adept at presenting technical ideas in the context of their time. On one level, Larry Bookman has produced a manuscript to satisfy the requirements of a PhD program. If that was all he did, my preface would praise the originality of his ideas and attempt to summarize their significance. But this book is much more than an accomplished disser tation about some aspect of natural language - it is also a skillfully crafted tour through a vast body of computational, linguistic, neurophysiological, and psychological research.
More details
Series
Edition
1994 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Product notice
sewn/stitched
Cloth over boards
Illustrations
XXI, 271 p.
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 18 mm
Weight
590 gr
ISBN-13
978-0-7923-9487-7 (9780792394877)
DOI
10.1007/978-1-4615-2780-0
Schweitzer Classification
Other editions
Additional editions
Lawrence A. Bookman
Trajectories through Knowledge Space
A Dynamic Framework for Machine Comprehension
E-Book
12/2012
Springer
€213.99
Available for download
Lawrence A. Bookman
Trajectories through Knowledge Space
A Dynamic Framework for Machine Comprehension
Book
10/2012
Springer
€213.99
Shipment within 7-9 days
Content
1 Introduction.- 1.1 Motivation.- 1.2 A View of Text Comprehension.- 1.3 Overview of the LeMICON System.- 1.4 Implementation.- 1.5 Points of Interest.- 1.6 The Impact of this Work on Four Related Fields.- 1.7 Development of the Two-Tier Model.- 1.8 A Guide to the Reader.- 2 An Overview of Connectionist and Probabilistic Approaches to Language Processing.- 2.1 A Computational Linguistics Perspective.- 2.2 A Connectionist Perspective.- 2.3 A Comparison of Connectionist and Probabilistic NLP Methods.- 2.4 Bridging the Gap - Integrating Connectionist and Computational Linguistic Approaches.- 3 Memory Architecture.- 3.1 The Relational Tier.- 3.2 The Associational or ASF Tier.- 3.3 Connecting the Two Tiers.- 3.4 Working Memory.- 3.5 How New Knowledge Is Integrated.- 4 The Basic Computation.- 4.1 A Functional Description of the Algorithm.- 4.2 Representing the Input.- 4.3 Computational Details and Program Output at each Step.- 4.4 General Discussion of the Algorithm.- 4.5 How LeMICON Handles Binding.- 4.6 The Links to Psychology and Neurophysiology Revisited.- 4.7 Some Comparisons to Other Text Understanding Systems.- 5 Analysis of the Interpretation at the Relational and ASF Level.- 5.1 Introduction.- 5.2 Analyzing the Interpretation at the Relational Level.- 5.3 Analyzing the Interpretation at the ASF Level.- 5.4 Analyzing Time-Dependent Interactions at the ASF Level.- 5.5 Comparing Interpretations - A Quantitative Analysis.- 5.6 An Ablation Study.- 6 Reasoning from the Relational Level of the Representation.- 6.1 Introduction.- 6.2 Identifying the Conceptual Roots.- 6.3 Explaining the Connections Between Events.- 6.4 Determining Important Concepts in the Interpretation Graph.- 6.5 Conceptual Roots and their Role in Summarization.- 7 Experiments in Acquiring Knowledgefrom On-line Corpora.- 7.1 Introduction.- 7.2 The Automatic Acquisition of Knowledge from On-line Sources.- 7.3 The Automatic Construction of the Relational Tier.- 7.4 The Automatic Construction of the Associational Tier.- 7.5 How Semantic Memory Evolves in Response to New Input.- 7.6 Changing the Link Weights.- 7.7 Implementation Details.- 8 An Analysis of the Acquired Knowledge.- 8.1 An Alternative View of the Underlying Knowledge Representation.- 8.2 Discussion of Soundness of Approach.- 8.3 An Evaluation of LeMICON's Representation.- 8.4 Previous Text Systems Revisited.- 8.5 The Knowledge Acquisition Continuum.- 9 Conclusions.- 9.1 Some Consequences of the Two-Tier Model of Memory.- 9.2 Associational Representations.- 9.3 The Universality of ASFs.- 9.4 Scalability.- 9.5 Automatic Acquisition of Knowledge.- 9.6 Building Large-Scale Knowledge Bases.- 9.7 A Link to Corpus Linguistics.- 9.8 The Interplay between Computation and Representation.- 9.9 Limitations.- 10 Future Directions.- 10.1 Expanding The Knowledge Base.- 10.2 Finding Deeper Semantic Relationships via Corpus Analysis.- 10.3 Handling Contradictory Input.- 10.4 Learning New Relationships.- 10.5 A Basic Level Semantic Encoding.- 10.6 Child Versus Adult Comprehension.- References.- Author Index.