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."

Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches. Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.

Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches. Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.

Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension provides an overview of many of the main ideas of connectionism (neural networks) and probabilistic natural language processing. Several areas of common overlap between these fields are described in which each community can benefit from the ideas and techniques of the other. The author's perspective on comprehension pulls together the most significant research of the last ten years and illustrates how we can move more forward onto the next level of intelligent text processing systems. A central focus of the book is the development of a framework for comprehension connecting research themes from cognitive psychology, cognitive science, corpus linguistics and artificial intelligence. The book proposes a new architecture for semantic memory, providing a framework for addressing the problem of how to represent background knowledge in a machine. This architectural framework supports a computational model of comprehension.Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension is an excellent reference for researchers and professionals, and may be used as an advanced text for courses on the topic.