Statistical Language Learning
Eugene Charniak, 1993

Chapter 1. The Standard Model

1.1 Two Technologies

1.2 Morphology and Knowledge of Words

1.3 Syntax and Context-Free Grammars

1.4 Chart Parsing

1.5 Meaning and Semantic Processing

1.6 Exercises

Chapter 2. Statistical Models and the Entropy of English

2.1 A Fragment of Probability Theory

2.2 Statistical Models

2.3 Speech Recognition

2.4 Entropy

2.5 Markov Chains

2.6 Cross Entropy

2.7 Cross Entropy as a Model Evaluator

2.8 Exercises

Chapter 3. Hidden Markov Models and Two Applications

3.1 Trigram Models of English

3.2 Hidden Markov Models

3.3 Part-of-Speech Tagging

3.4 Exercises

Chapter 4. Algorithms for Hidden Markov Models

4.1 Finding the Most Likely Path

4.2 Computing HMM Output Probabilities

4.3 HMM Training

4.4 Exercises

Chapter 5. Probabalistic Context-Free Grammars

5.1 Probabilistic Grammars

5.2 PCFGs and Syntactic Ambiguity

5.3 PCFGs and Grammar Induction

5.4 PCFGs and Ungrammaticality

5.5 PCFGs and Language Modeling

5.6 Basic ALgorithms for PCFGs

5.7 Exercises

Chapter 6. The Mathematics of PCFGs

6.1 Relation of HMMs to PCFGs

6.2 Finding Sentence Probabilities for PCFGs

6.3 Training PCFGs

6.4 Exercises

Chapter 7. Learning Probabilistic Grammars

7.1 Why the Simple Approach Fails

7.2 Learning Dependency Grammars

7.3 Learning from a Bracketed Corpus

7.4 Improving a Partial Grammar

7.5 Exercises

Chapter 8. Syntactic Disambiguation

8.1 Simple Methods for Prepositional Phrases

8.2 Using Semantic Information

8.3 Relative-Clause Attachment

8.4 Uniform Use of Lexical/Semantic Information

8.5 Exercises

Chapter 9. Word Classes and Meaning

9.1 Clustering

9.2 Clustering by Next Word

9.3 Clustering with Syntactic Information

9.4 Problems with Word Clustering

9.5 Exercises

Chapter 10. Word Senses and Their Disambiguation

10.1 Word Senses Using Outside Information

10.2 Word Senses Without Outside Information

10.3 Meanings and Selectional Restrictions

10.4 Discussion

10.5 Exercises


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