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This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence -- a synthesis that could lead to new insights into both representation and learning.

This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence -- a synthesis that could lead to new insights into both representation and learning.

The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are inspired by the network-like architecture of the brain. Because of their unique architecture and style of processing, connectionist systems are generally regarded as radically different from the more traditional symbol manipulation models. This collection was designed to provide philosophers who have been working in the area of cognitive science with a forum for expressing their views on these recent developments. Because the symbol-manipulating paradigm has been so important to the work of contemporary philosophers, many have watched the emergence of connectionism with considerable interest. The contributors take very different stands toward connectionism, but all agree that the potential exists for a radical shift in the way many philosophers think of various aspects of cognition. Exploring this potential and other philosophical dimensions of connectionist research is the aim of this volume.

Connectionism is a way of modeling how the brain uses streams of sensory inputs to understand the world and produce behavior, based on cognitive processes which actually occur. This book describes the principles, and their application to explaining how the brain produces speech, forms memories and recognizes faces, how intellect develops, and how it deteriorates after brain damage. The models range from some well-known classics to others at the frontiers of current research. Also included is a disk with the software for running tlearn, a user-friendly simulator for connectionist modeling of cognitive processes, which will run on either PCs or Macs. Written by leading researchers in their field, this first up-to-date textbook on connectionist modeling, will provide an essential and accessible introduction to the field.

These two volumes consist of chapters written by students and colleagues of W.K. Estes. The books' contributors -- themselves eminent figures in the field -- reflect on Estes' sweeping contributions to mathematical as well as cognitive and experimental psychology. As indicated by their titles, Volume I features mathematical and theoretical essays, and Volume II presents cognitive and experimental essays. Both volumes contain insightful literature reviews as well as descriptions of exciting new theoretical and empirical advances. Many of the essays also incorporate personal reminiscences reflecting the authors' fond affection for their illustrious mentor.

This text focuses on the use of computer simulation models in cognitive approaches to human language processing. A substantial range of computer models is presented and compared. The characteristic focus points of current trends in psycholinguistics are all covered. Firstly, psycholinguistics has a strong computational element and computer models can no longer be ignored in the description of important psycholinguistic theory. Secondly, the research area has recently seen a clash between the established "symbolic" view and the more recent "subsymbolic" connectionist approach to psycholinguistics. Thirdly, psycholinguistics has become an area of research that can be viewed as multidisciplinary, where co-operation takes place with researchers in linguistics as well as more diverse fields such as computer science, engineering, biophysics and philosophy.

Rethinking Innateness asks the question, "What does it really mean to say that a behavior is innate?" The authors describe a new framework in which interactions, occurring at all levels, give rise to emergent forms and behaviors. These outcomes often may be highly constrained and universal, yet are not themselves directly contained in the genes in any domain-specific way.One of the key contributions of Rethinking Innateness is a taxonomy of ways in which a behavior can be innate. These include constraints at the level of representation, architecture, and timing; typically, behaviors arise through the interaction of constraints at several of these levels.The ideas are explored through dynamic models inspired by a new kind of "developmental connectionism," a marriage of connectionist models and developmental neurobiology, forming a new theoretical framework for the study of behavioral development. While relying heavily on the conceptual and computational tools provided by connectionism, Rethinking Innateness also identifies ways in which these tools need to be enriched by closer attention to biology.

This book is the companion volume to Rethinking Innateness: A Connectionist Perspective on Development (The MIT Press, 1996), which proposed a new theoretical framework to answer the question "What does it mean to say that a behavior is innate?" The new work provides concrete illustrations―in the form of computer simulations―of properties of connectionist models that are particularly relevant to cognitive development. This enables the reader to pursue in depth some of the practical and empirical issues raised in the first book. The authors' larger goal is to demonstrate the usefulness of neural network modeling as a research methodology.The book comes with a complete software package, including demonstration projects, for running neural network simulations on both Macintosh and Windows 95. It also contains a series of exercises in the use of the neural network simulator provided with the book. The software is also available to run on a variety of UNIX platforms.

Neural Networks in C++ An Object-Oriented Framework for Building Connectionist Systems Extremely useful, this valuable guide concentrates on the practical side of building neural network applications. Written with a wealth of useful examples in C++, the book provides you with the nuts-and-bolts guidelines for hands-on development of real-world connectionist systems. Neural Networks in C++ also: Includes a complete C++ class library especially designed for neural network applications Fully covers three major application areas of neural network programming—image recognition, text processing, and forecasting Provides introductory material on neural networks Covers the basics on an object-oriented framework for connectionist systems

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Since its inception some fifty years ago, cognitive science has seen a number of sea changes. Perhaps the best known is the development of connectionist models of cognition as an alternative to classical, symbol-based approaches. A more recent - and increasingly influential - trend is that of dynamical-systems-based, ecologically oriented models of the mind. Researchers suggest that a full understanding of the mind will require systematic study of the dynamics of interaction between mind, body, and world. Some argue that this new orientation calls for a revolutionary new metaphysics of mind, according to which mental states and processes, and even persons, literally extend into the environment. This is a state-of-the-art guide to this new movement in cognitive science. Each chapter tackles either a specific area of empirical research or specific sector of the conceptual foundation underlying this research.

Referencing new developments in cognitive and functional linguistics, phonetics, and connectionist modeling, this book investigates various ways in which a speaker/hearer's experience with language affects the representation of phonology. Rather than assuming phonological representations in terms of phonemes, Joan Bybee adopts an exemplar model, in which specific tokens of use are stored and categorized phonetically with reference to variables in the context. This model allows an account of phonetically gradual sound change that produces lexical variation, and provides an explanatory account of the fact that many reductive sound changes affect high frequency items first.

"Culture" and "meaning" are central to anthropology, but anthropologists do not agree on what they are. Claudia Strauss and Naomi Quinn propose a new theory of cultural meaning, one that gives priority to the way people's experiences are internalized. Drawing on "connectionist" or "neural network" models as well as other psychological theories, they argue that cultural meanings are not fixed or limited to static groups, but neither are they constantly revised or contested. Their approach is illustrated by original research on understandings of marriage and ideas of success in the United States.

These 31 essays define and explore the concept of emergent computation in such areas as artificial networks, adaptive systems, classifier systems, connectionist learning, other learning, and biological networks to determine what properties are required of the supporting architectures that generate them.Researchers in several fields are exploring computational systems in which interesting global behavior emerges from local interactions among component parts - an approach called emergent computation. In these systems, interactions among simultaneous computations are exploited to improve efficiency, increase flexibility, or provide more realistic models of natural phenomena. These 31 essays define and explore the concept of emergent computation in such areas as artificial networks, adaptive systems, classifier systems, connectionist learning, other learning, and biological networks to determine what properties are required of the supporting architectures that generate them. Many of the essays share the themes of design (how to construct such systems), the importance of preexisting structure to learning and the role of parallelism, and the tension between cooperative and competitive models of interaction. In the introduction, Stephanie Forrest presents several detailed examples of the kinds of problems emergent computation can address. These include showing how emergent computation can lead to efficiency improvements in parallel processing, establishing the connection between emergent computation and nonlinear systems, and comparing two search techniques to show how the emergent-computational approach to a problem differs from other more conventional approaches.

Perceptrons - the first systematic study of parallelism in computation - has remained a classical work on threshold automata networks for nearly two decades. It marked a historical turn in artificial intelligence, and it is required reading for anyone who wants to understand the connectionist counterrevolution that is going on today. Artificial-intelligence research, which for a time concentrated on the programming of ton Neumann computers, is swinging back to the idea that intelligence might emerge from the activity of networks of neuronlike entities. Minsky and Papert's book was the first example of a mathematical analysis carried far enough to show the exact limitations of a class of computing machines that could seriously be considered as models of the brain. Now the new developments in mathematical tools, the recent interest of physicists in the theory of disordered matter, the new insights into and psychological models of how the brain works, and the evolution of fast computers that can simulate networks of automata have given Perceptrons new importance.Witnessing the swing of the intellectual pendulum, Minsky and Papert have added a new chapter in which they discuss the current state of parallel computers, review developments since the appearance of the 1972 edition, and identify new research directions related to connectionism. They note a central theoretical challenge facing connectionism: the challenge to reach a deeper understanding of how "objects" or "agents" with individuality can emerge in a network. Progress in this area would link connectionism with what the authors have called "society theories of mind." Marvin L. Minsky is Donner Professor of Science in M.I.T.'s Electrical Engineering and Computer Science Department. Seymour A. Papert is Professor of Media Technology at M.I.T. .

Since its inception some fifty years ago, cognitive science has seen a number of sea changes. Perhaps the best known is the development of connectionist models of cognition as an alternative to classical, symbol-based approaches. A more recent - and increasingly influential - trend is that of dynamical-systems-based, ecologically oriented models of the mind. Researchers suggest that a full understanding of the mind will require systematic study of the dynamics of interaction between mind, body, and world. Some argue that this new orientation calls for a revolutionary new metaphysics of mind, according to which mental states and processes, and even persons, literally extend into the environment. This is a state-of-the-art guide to this new movement in cognitive science. Each chapter tackles either a specific area of empirical research or specific sector of the conceptual foundation underlying this research.

Philosophers and cognitive scientists reassess systematicity in the post-connectionist era, offering perspectives from ecological psychology, embodied and distributed cognition, enactivism, and other methodologies.In 1988, Jerry Fodor and Zenon Pylyshyn challenged connectionist theorists to explain the systematicity of cognition. In a highly influential critical analysis of connectionism, they argued that connectionist explanations, at best, can only inform us about details of the neural substrate; explanations at the cognitive level must be classical insofar as adult human cognition is essentially systematic. More than twenty-five years later, however, conflicting explanations of cognition do not divide along classicist-connectionist lines, but oppose cognitivism (both classicist and connectionist) with a range of other methodologies, including distributed and embodied cognition, ecological psychology, enactivism, adaptive behavior, and biologically based neural network theory. This volume reassesses Fodor and Pylyshyn's “systematicity challenge” for a post-connectionist era.The contributors consider such questions as how post-connectionist approaches meet Fodor and Pylyshyn's conceptual challenges; whether there is empirical evidence for or against the systematicity of thought; and how the systematicity of human thought relates to behavior. The chapters offer a representative sample and an overview of the most important recent developments in the systematicity debate.ContributorsKen Aizawa, William Bechtel, Gideon Borensztajn, Paco Calvo, Anthony Chemero, Jonathan D. Cohen, Alicia Coram, Jeffrey L. Elman, Stefan L. Frank, Antoni Gomila, Seth A. Herd, Trent Kriete, Christian J. Lebiere, Lorena Lobo, Edouard Machery, Gary Marcus, Emma Martín, Fernando Martínez-Manrique, Brian P. McLaughlin, Randall C. O'Reilly, Alex A. Petrov, Steven Phillips, William Ramsey, Michael Silberstein, John Symons, David Travieso, William H. Wilson, Willem Zuidema

Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf