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英语语料库与自动语法分析 版权信息
- ISBN：9787100056595
- 条形码：9787100056595 ; 978-7-100-05659-5
- 装帧：暂无
- 册数：暂无
- 重量：暂无
- 所属分类：>
英语语料库与自动语法分析 内容简介
语料库语言学和计算语言学为促进自然语言处理技术快速发展的两门基础学科。《英语语料库与自动语法分析》系这两个领域的一本专著，它以国际英语语料库为背景，着重探讨大型语料库的语法分析，尤其是英语口语材料给计算机自动处理带来的一系列难题。书中涉及基于概率的自动词类识别和基于实例的自动句法分析这两大技术，并有专门章节来探讨句法分析的评测问题，对AUTASYS和The Survey Parser这两个软件系统的实际表现进行了深入的量化评测。此外，本书还探讨了介词短语的自动分析，特别是这类短语的句法功能的自动判定，并对自动语法分析在语音合成及语音识别中的应用做了相应的说明。
英语语料库与自动语法分析 目录
Preface
前言
List of Figures
List of Tables
Abstract
1. Introduction
1.1. What is Parsing?
1.2. The Introspective View
1.3. The Retrospective View
1.4. Data-Oriented Parsing
1.5. General Problems
1.6. The Proposed Research
1.6.1. Background to the Proposed Research
1.6.2. The Basic Approach of the Proposed Research
1.6.3. The Strengths and Novelties of the Proposed Approach
1.6.3.1. Automated Grammar Generation
1.6.3.2. De-Lexicalised Terminal Nodes
1.6.3.3. Global Parse with Subcategorisation Features
1.6.3.4. High-Quality Partial Parse
1.6.3.5. Intrinsic Ability to Learn
1.7. The Organisation of the Book
2. The Automatic Analysis of English Word Classes
2.1. An Overview of Word Class Tagging
2.2. Major Word Class Tagging Schemes
2.2.1. The Lancaster-Oslo/Bergen Tagging Scheme
2.2.1.1. The Lancaster-Oslo-Bergen Corpus
2.2.1.2. The Lancaster-Oslo-Bergen Tag Set
2.2.1.3. Summary
2.2.2. The International Corpus of English Tagging Scheme
2.2.2.1. The International Corpus of English
2.2.2.2. The International Corpus of English Tag Set
2.2.3. A Comparison of LOB and ICE
2.3. Word Class Tagging Methodologies
2.3.1. The Rule-Based Approach
2.3.2. The Probabilistic Approach
2.4. AUTASYS: A Hybrid Tagging System
2.4.1. A Probabilistic Approach Using the LOB Tag Set
2.4.1.1. The Tag Assignment Module
2.4.1.1.1. Tokenisation
2.4.1.1.2. The treatment of"."
2.4.1.1.3. The treatment of"'"
2.4.1.1.4. Sentence boundary markers
2.4.1.2. Orthographic Analysis
2.4.1.3. Lexicon Lookup
2.4.1.3.1. The lexicon
2.4.1.3.2. The coverage of the lexicon
2.4.1.4. Morphological Analysis
2.4.2. The Idiom Identification Module
2.4.3. The Probabilistic Tag Selection Module
2.4.3.1. The Bigram Probabilistic Matrix
2.4.3.2. Implementing Probabilistic Tag Selection
2.4.4. The Rule-Based Refinement Module
2.4.5. Empirical Evaluation
2.4.6. Permissive AUTASYS-LOB Disagreements
2.4.6.1. NNP-NPT
2.4.6.2. JJ-JJB
2.4.6.3. NNP-NPL
2.4.6.4. RB-NN
2.4.7. Summary
2.5. A Rule-Based Approach towards LOB to ICE Translation
2.5.1. Solutions for Verbs
2.5.1.1. Auxiliary vs. Lexical
2.5.1.2. Monotransitive vs. Complex Transitive
2.5.1.3. Finite vs. Nonfinite
2.5.2. Closed Sets
2.5.3. Initial Results
2.5.4. Problems
2.5.5. Summary
3. The Automatic Induction of a Formal Grammar
4. Robust Practical Analogy-Based Parsing
5. Extensive Evaluations of the Survey Parser
6. The Resolution of Prepositional Phrases
7. Conclusions and Further Work
References
Appendix A: A List of LOB Tags
Appendix B: A List of ICE Tags
Appendix C: A List of AUTASYS Idioms
Appendix D: A List of ICE Parsing Symbols
Appendix E: A List of ICE Prepositions in Descending Frequency Order
Appendix F: A Distributional Profile of ICE-GB Prepositions
Index
前言
List of Figures
List of Tables
Abstract
1. Introduction
1.1. What is Parsing?
1.2. The Introspective View
1.3. The Retrospective View
1.4. Data-Oriented Parsing
1.5. General Problems
1.6. The Proposed Research
1.6.1. Background to the Proposed Research
1.6.2. The Basic Approach of the Proposed Research
1.6.3. The Strengths and Novelties of the Proposed Approach
1.6.3.1. Automated Grammar Generation
1.6.3.2. De-Lexicalised Terminal Nodes
1.6.3.3. Global Parse with Subcategorisation Features
1.6.3.4. High-Quality Partial Parse
1.6.3.5. Intrinsic Ability to Learn
1.7. The Organisation of the Book
2. The Automatic Analysis of English Word Classes
2.1. An Overview of Word Class Tagging
2.2. Major Word Class Tagging Schemes
2.2.1. The Lancaster-Oslo/Bergen Tagging Scheme
2.2.1.1. The Lancaster-Oslo-Bergen Corpus
2.2.1.2. The Lancaster-Oslo-Bergen Tag Set
2.2.1.3. Summary
2.2.2. The International Corpus of English Tagging Scheme
2.2.2.1. The International Corpus of English
2.2.2.2. The International Corpus of English Tag Set
2.2.3. A Comparison of LOB and ICE
2.3. Word Class Tagging Methodologies
2.3.1. The Rule-Based Approach
2.3.2. The Probabilistic Approach
2.4. AUTASYS: A Hybrid Tagging System
2.4.1. A Probabilistic Approach Using the LOB Tag Set
2.4.1.1. The Tag Assignment Module
2.4.1.1.1. Tokenisation
2.4.1.1.2. The treatment of"."
2.4.1.1.3. The treatment of"'"
2.4.1.1.4. Sentence boundary markers
2.4.1.2. Orthographic Analysis
2.4.1.3. Lexicon Lookup
2.4.1.3.1. The lexicon
2.4.1.3.2. The coverage of the lexicon
2.4.1.4. Morphological Analysis
2.4.2. The Idiom Identification Module
2.4.3. The Probabilistic Tag Selection Module
2.4.3.1. The Bigram Probabilistic Matrix
2.4.3.2. Implementing Probabilistic Tag Selection
2.4.4. The Rule-Based Refinement Module
2.4.5. Empirical Evaluation
2.4.6. Permissive AUTASYS-LOB Disagreements
2.4.6.1. NNP-NPT
2.4.6.2. JJ-JJB
2.4.6.3. NNP-NPL
2.4.6.4. RB-NN
2.4.7. Summary
2.5. A Rule-Based Approach towards LOB to ICE Translation
2.5.1. Solutions for Verbs
2.5.1.1. Auxiliary vs. Lexical
2.5.1.2. Monotransitive vs. Complex Transitive
2.5.1.3. Finite vs. Nonfinite
2.5.2. Closed Sets
2.5.3. Initial Results
2.5.4. Problems
2.5.5. Summary
3. The Automatic Induction of a Formal Grammar
4. Robust Practical Analogy-Based Parsing
5. Extensive Evaluations of the Survey Parser
6. The Resolution of Prepositional Phrases
7. Conclusions and Further Work
References
Appendix A: A List of LOB Tags
Appendix B: A List of ICE Tags
Appendix C: A List of AUTASYS Idioms
Appendix D: A List of ICE Parsing Symbols
Appendix E: A List of ICE Prepositions in Descending Frequency Order
Appendix F: A Distributional Profile of ICE-GB Prepositions
Index
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