The Handbook of Computational Linguistics and Natural Language Processing.

The Handbook of Computational Linguistics and Natural Language Processing.

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Bibliographic Details
Main Author: Clark, Alexander
Other Authors: Fox, Chris, Lappin, Shalom
Format: eBook
Language:English
Published: Somerset : Wiley, 2013.
Series:Blackwell handbooks in linguistics.
Subjects:
Computational linguistics.
Natural language processing (Computer science)
computational linguistics.
Computational linguistics
Online Access:Click for online access
Table of Contents:
  • Praise for The Handbook of Computational Linguistics and Natural Language Processing; Series page; Dedication; Title page; Copyright page; List of Figures; List of Tables; Notes on Contributors; Preface; Introduction; Part I: Formal Foundations; 1 Formal Language Theory; 1 Introduction; 2 Basic Notions; 3 Language Classes and Linguistic Formalisms; 4 Regular Languages; 5 Context-Free Languages; 6 The Chomsky Hierarchy; 7 Mildly Context-Sensitive Languages; 8 Further Reading; 2 Computational Complexity in Natural Language; 1 A Brief Review of Complexity Theory; 2 Parsing and Recognition.
  • 3 Complexity and Semantics4 Determining Logical Relationships between Sentences; 3 Statistical Language Modeling; 1 Introduction to Statistical Language Modeling; 2 Structured LanguageModel; 3 Speech Recognition Lattice Rescoring Using the Structured Language Model; 4 Richer Syntactic Dependencies; 5 Comparison with Other Approaches; 6 Conclusion; 4 Theory of Parsing; 1 Introduction; 2 Context-Free Grammars and Recognition; 3 Context-Free Parsing; 4 Probabilistic Parsing; 5 Lexicalized Context-Free Grammars; 6 Dependency Grammars; 7 Tree Adjoining Grammars; 8 Translation; 9 Further Reading.
  • Part II: Current Methods5 Maximum Entropy Models; 1 Introduction; 2 Maximum Entropy and Exponential Distributions; 3 Parameter Estimation; 4 Regularization; 5 Model Applications; 6 Prospects; 6 Memory-Based Learning; 1 Introduction; 2 Memory-Based Language Processing; 3 NLP Applications; 4 Exemplar-Based Computational Psycholinguistics; 5 Generalization and Abstraction; 6 Generalizing Examples; 7 Further Reading; 7 Decision Trees; 1 NLP and Classification; 2 Induction of Decision Trees; 3 NLP Applications; 4 Advantages and Disadvantages of Decision Trees; 5 Further Reading.
  • 8 Unsupervised Learning and Grammar Induction1 Overview; 2 Computational Learning Theory; 3 Empirical Learning; 4 Unsupervised Grammar Induction and Human Language Acquisition; 5 Conclusion; 9 Artificial Neural Networks; 1 Introduction; 2 Background; 3 Contemporary Research; 4 Further Reading; 10 Linguistic Annotation; 1 Introduction; 2 Review of Selected Annotation Schemes; 3 The Annotation Process; 4 Conclusion; 11 Evaluation of NLP Systems; 1 Introduction; 2 Fundamental Concepts; 3 Evaluation Paradigms in Common Evaluation Settings; 4 Case Study: Evaluation ofWord-Sense Disambiguation.
  • 5 Case Study: Evaluation of Question Answering Systems6 Summary; Part III: Domains of Application; 12 Speech Recognition; 1 Introduction; 2 Acoustic Modeling; 3 Search; 4 Case Study: The AMI System; 5 Current Topics; 6 Conclusions; 13 Statistical Parsing; 1 Introduction; 2 History; 3 Generative Parsing Models; 4 Discriminative Parsing Models; 5 Transition-Based Approaches; 6 Statistical Parsing with CCG; 7 OtherWork; 8 Conclusion; 14 Segmentation and Morphology; 1 Introduction; 2 Unsupervised Learning ofWords; 3 Unsupervised Learning of Morphology; 4 Implementing ComputationalMorphologies.

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