From: wermter@informatik.uni-hamburg.de (Stefan Wermter) Newsgroups: comp.ai,comp.ai.nat-lang,comp.ai.neural-nets,de.sci.ki.announce Subject: book on hybrid connectionist language processing Date: 13 Dec 1994 12:38:56 GMT BOOK ANNOUNCEMENT ----------------- The following book is now available from the beginning of December 1994. Title: Hybrid connectionist natural language processing Date: 1995 Author: Stefan Wermter Dept. of Computer Science University of Hamburg Vogt-Koelln-Str. 30 D-22527 Hamburg Germany wermter@informatik.uni-hamburg.de Series: Neural Computing Series 7 Publisher: Chapman & Hall Inc 2-6 Boundary Row London SE1 8HN England (Order information in the end of this message) Description ----------- The objective of this book is to describe a new approach in hybrid connectionist natural language processing which bridges the gap between strictly symbolic and connectionist systems. This objective is tackled in two ways: the book gives an overview of hybrid connectionist archi- tectures for natural language processing; and it demonstrates that a hybrid connectionist architecture can be used for learning real-world natural language problems. The book is primarily intended for scientists and students interested in the fields of artificial intelligence, neural networks, connectionism, natural language processing, hybrid symbolic connectionist architectures, parallel distributed processing, machine learning, automatic knowledge acquisition or computational linguistics. Furthermore, it might be of interest for scientists and students in information retrieval and cognitive science, since the book points out interdisciplinary relationships to these fields. We develop a systematic spectrum of hybrid connectionist architectures, >from completely symbolic architectures to separated hybrid connectionist architectures, integrated hybrid connectionist architectures and completely connectionist architectures. Within this systematic spectrum we have designed a system SCAN with two separated hybrid connectionist architectures and two integrated hybrid connectionist architectures for a scanning understanding of phrases. A scanning understanding is a relation-based flat understanding in contrast to traditional symbolic in-depth understanding. Hybrid connectionist representations consist of either a combination of connectionist and symbolic representations or different connectionist representations. In particular, we focus on important tasks like structural disambiguation and semantic context classification. We show that a parallel modular, constraint-based, plausibility-based and learned use of multiple hybrid connectionist representations provides powerful architectures for learning a scanning understanding. In particular, the combination of direct encoding of domain-independent structural knowledge and the connectionist learning of domain-dependent semantic knowledge, as suggested by a scanning under- standing in SCAN, provides concepts which lead to flexible, adaptable, transportable architectures for different domains. Table of Contents ----------------- 1 Introduction 1.1 Learning a Scanning Understanding 1.2 The General Approach 1.3 Towards a Hybrid Connectionist Memory Organization 1.4 An Overview of the SCAN Architecture 1.5 Organization and Reader's Guide 2 Connectionist and Hybrid Models for Language Understanding 2.1 Foundations of Connectionist and Hybrid Connectionist Approaches 2.2 Connectionist Architectures 2.2.1 Representation of Language in Parallel Spatial Models Early Pattern Associator for Past Tense Learning Pattern Associator for Semantic Case Assignment Pattern Associator with Sliding Window Time Delay Neural Networks 2.2.2 Representation of Language in Recurrent Models Recurrent Jordan Network for Action Generation Simple Recurrent Network for Sequence Processing Recursive Autoassociative Memory Network 2.2.3 Towards Modular and Integrated Connectionist Models Cascaded Networks Sentence Gestalt Model Grounding Models 2.3 Hybrid Connectionist Architectures 2.3.1 Sentence Analysis in Hybrid Models Hybrid Interactive Model for Constraint Integration Hybrid Model for Sentence Analysis 2.3.2 Inferencing in Hybrid Models Symbolic Marker Passing and Localist Networks Symbolic Reasoning with Connectionist Models 2.3.3 Architectural Issues in Hybrid Connectionist Systems Symbolic Neuroengineering and Symbolic Recirculation Modular Model for Parsing 2.4 Summary and Discussion 3 A Hybrid Connectionist Scanning Understanding of Phrases 3.1 Foundations of a Hybrid Connectionist Architecture 3.1.1 Motivation for a Hybrid Connectionist Architecture 3.1.2 The Computational Theory Level for a Scanning Understanding 3.1.3 Constraint Integration 3.1.4 Plausibility view 3.1.5 Learning 3.1.6 Subtasks of Scanning Understanding at the Computational Theory Level 3.1.7 The Representation Level for a Scanning Understanding 3.2 Corpora and Lexicon for a Scanning Understanding 3.2.1 The Underlying Corpora 3.2.2 Complex Phrases 3.2.3 Context and Ambiguities of Phrases 3.2.4 Organization of the Lexicon 3.3 Plausibility Networks 3.3.1 Learning Semantic Relationships and Semantic Context 3.3.2 The Foundation of Plausibility Networks 3.3.3 Plausibility Networks for Noun-Connecting Semantic Relationships 3.3.4 Learning in Plausibility Networks 3.3.5 Recurrent Plausibility Networks for Contextual Relationships 3.3.6 Learning in Recurrent Plausibility Networks 3.4 Summary and Discussion 4 Structural Phrase Analysis in a Hybrid Separated Model 4.1 Introduction and Overview 4.2 Constraints for Coordination 4.3 Symbolic Representation of Syntactic Constraints 4.3.1 A Grammar for Complex Noun Phrases 4.3.2 The Active Chart Parser and the Syntactic Constraints 4.4 Connectionist Representation of Semantic Constraints 4.4.1 Head-noun Structure for Semantic Relationships 4.4.2 Training and Testing Plausibility Networks with NCN-relationships 4.4.3 Learned Internal Representation 4.5 Combining Chart Parser and Plausibility Networks 4.6 A Case Study 4.7 Summary and Discussion 5 Structural Phrase Analysis in a Hybrid Integrated Model 5.1 Introduction and Overview 5.2 Constraints for Prepositional Phrase Attachment 5.3 Representation of Constraints in Relaxation Networks 5.3.1 Integrated Relaxation Network 5.3.2 The Relaxation Algorithm 5.3.3 Testing Relaxation Networks 5.4 Representation of Semantic Constraints in Plausibility Networks 5.4.1 Training and Testing Plausibility Networks with NPN-Relationships 5.4.2 Learned Internal Representation 5.5 Combining Relaxation Networks and Plausibility Networks 5.5.1 The Interface between Relaxation Networks and Plausibility Networks 5.5.2 The Dynamics of Processing in a Relaxation Network 5.6 A Case Study 5.7 Summary and Discussion 6 Contextual Phrase Analysis in a Hybrid Separated Model 6.1 Introduction and Overview 6.2 Towards a Scanning Understanding of Semantic Phrase Context 6.2.1 Superficial Classification in Information Retrieval 6.2.2 Skimming Classification with Symbolic Matching 6.3 Constraints for Semantic Context Classification of Noun Phrases 6.4 Syntactic Condensation of Phrases to Compound Nouns 6.4.1 Motivation of Symbolic Condensation 6.4.2 Condensation Using a Symbolic Chart Parser 6.5 Plausibility Networks for Context Classification of Compound Nouns 6.5.1 Training and Testing the Recurrent Plausibility Network 6.5.2 Learned Internal Representation 6.6 Summary and Discussion 7 Contextual Phrase Analysis in a Hybrid Integrated Model 7.1 Introduction and Overview 7.2 Constraints for Semantic Context Classification of Phrases 7.3 Plausibility Networks for Context Classification of Phrases 7.3.1 Training and Testing with Complete Phrases 7.3.2 Training and Testing with Phrases without Insignificant Words 7.3.3 Learned Internal Representation 7.4 Semantic Context Classification and Text Filtering 7.5 Summary and Discussion 8 General Summary and Discussion 8.1 The General Framework of SCAN 8.2 Analysis and Evaluation 8.2.1 Evaluating the Problems 8.2.2 Evaluating the Methods 8.2.3 Evaluating the Representations 8.2.4 Evaluating the Experiment Design 8.2.5 Evaluating the Experiment Results 8.3 Extensions of a Scanning Understanding 8.3.1 Extending Modular Subtasks 8.3.2 Extending Interactions 8.4 Contributions and Conclusions 9 Appendix 9.1 Hierarchical Cluster Analysis 9.2 Implementation 9.3 Examples of Phrases for Structural Phrase Analysis 9.4 Examples of Phrases for Contextual Phrase Analysis References Index Orders information ----------------- ISBN: 0 412 59100 6 Pages: 190 Figures: 56 Price: 29.95 pounds sterling, 52.00 US dollars Credit cards: all major credit cards accepted by Chapman & Hall Please order from: -- Pam Hounsome Chapman & Hall Cheriton House North Way Andover Hants, SP10 5BE, UK England UK orders: Tel: 01264 342923 Fax: 01264 364418 Overseas orders: Tel: +44 1264 342830 Fax: +44 1264 342761 (Sister company in US) -- Peter Clifford Chapman & Hall Inc One Penn Plaza 41st Floor New York NY 10119 USA Tel: 212 564 1060 Fax: 212 564 1505 -- or 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