| 本论文研究的是机器翻译，而且探索摆脱基于符号系统的机器翻译和基于语料库统计的机器翻译模式，在机器翻译中应用人工神经网络的技术，具有开创性的意义，作者提出用分布式神经网络体系解决翻译模式的训练，以解决单一网络学习能力有限的问题，对神经网络语言处理技术开发了新思路，颇发人深思。 |
| Preface Acknowledgements Chapter One Prologue Chapter Two MT state of the art 2.1 MT as symbolic systems 2.2 Practical MT 2.3 Alternative technique of MT 2.3.1 Theoretical foundation 2.3.2 Translation model 2.3.3 Language model 2.4 Discussion Chapter Three Connectionist solutions 3.1 NLP models 3.2 Representation 3.3 Phonological processing 3.4 Learning verb past tense 3.5 Part of speech tagging 3.6 Chinese collocation learning 3.7 Syntactic parsing 3.7.1 Learning active/passive transformation 3.7.2 Confluent preorder parsing 3.7.3 Parsing with fiat structures 3.7.4 Parsing embedded clauses 3.7.5 Parsing with deeper structures 3.8 Discourse analysis 3.8.1 Story gestalt and text understanding 3.8.2 Processing stories with scriptural knowledge 3.9 Machine translation 3.10 Conclusion Chapter Four NeuroTrans design considerations 4.1 Scalability and extensibility 4.2 Transfer or inter lingual 4.3 Hybrid or fully connectionist 4.4 The use of linguistic knowledge 4.5 Translation as a two stage process 4.6 Selection of network models 4.7 Connectionist implementation 4.8 Connectionist representation issues 4.9 Conclusion Chapter Five A neural lexicon model 5.1 Language data 5.2 Knowledge representation 5.2.1 Symbolic approach 5.2.2 The statistical approach 5.2.3 Connectionist approach 5.2.4 NeuroTrans' input/output representation 5.2.5 NeuroTrans' lexicon representation 5.3 Implementing the neural lexicon 5.3.1 Words in context 5.3.2 Context with weights 5.3.3 Details of algorithm 5.3.4 The Neural Lexicon Builder 5.4 Training 5.4.1 Sample preparation 5.4.2 Training results 5.4.3 Generalization test 5.5 Discussion 5.5.1 Adequacy 5.5.2 Scalability and Extensibility 5.5.3 Efficiency 5.5.4 Weaknesses Chapter Six Implementing the language model 6.1 Overview 6.2 Design 6.2.1 Redefining the generation problem 6.2.2 Defining jumble activity 6.2.3 Language model structure 6.3 Implementation 6.3.1 Network structure Sampling Training and results 6.3.2 Generalization test 6.4 Discussion 6.4.1 Insufficient data 6.4.2 Information richness 6.4.3 Insufficient contextual information 6.4.4 Distributed language model Chapter Seven Conclusion Chapter Eight References Index |
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