Connection science is a new information-processing paradigm which attempts to imitate the architecture and process of the brain, and brings together researchers from disciplines as diverse as computer science, physics, psychology, philosophy, linguistics, biology, engineering, neuroscience and AI. Work in Connectionist Natural Language Processing (CNLP) is now expanding rapidly, yet much of the work is still only available in journals, some of them quite obscure. To make this research more accessible this book brings together an important and comprehensive set of articles from the journal CONNECTION SCIENCE which represent the state of the art in Connectionist natural language processing; from speech recognition to discourse comprehension. While it is quintessentially Connectionist, it also deals with hybrid systems, and will be of interest to both theoreticians as well as computer modellers.
Range of topics covered:

Connectionism and Cognitive Linguistics
Motion, Chomsky's Government-binding Theory
Syntactic Transformations on Distributed Representations
Syntactic Neural Networks
A Hybrid Symbolic/Connectionist Model for Understanding of Nouns
Connectionism and Determinism in a Syntactic Parser
Context Free Grammar Recognition
Script Recognition with Hierarchical Feature Maps
Attention Mechanisms in Language
Script-Based Story Processing
A Connectionist Account of Similarity in Vowel Harmony
Learning Distributed Representations
Connectionist Language Users
Representation and Recognition of Temporal Patterns
A Hybrid Model of Script Generation
Networks that Learn about Phonological Features
Pronunciation in Text-to-Speech Systems