Textual Information Access: Statistical Models

Table of Contents

Introduction

PART 1: INFORMATION RETRIEVAL

Chapter 1. Probabilistic Models for Information Retrieval

1.1. Introduction

1.2. 2-Poisson models

1.3. Probability ranking principle (PRP)

1.4. Language models

1.5. Informational approaches

1.6. Experimental comparison

1.7. Tools for information retrieval

1.8. Conclusion

1.9. Bibliography

Chapter 2. Learnable Ranking Models for Automatic Text Summarization and Information Retrieval

2.1. Introduction

2.2. Application to automatic text summarization

2.3. Application to information retrieval

2.4. Conclusion

2.5. Bibliography

PART 2: CLASSIFICATION AND CLUSTERING

Chapter 3. Logistic Regression and Text Classification

3.1. Introduction

3.2. Generalized linear model

3.3. Parameter estimation

3.4. Logistic regression

3.5. Model selection

3.6. Logistic regression applied to text classification

3.7. Conclusion

3.8. Bibliography

Chapter 4. Kernel Methods for Textual Information Access

4.1. Kernel methods: context and intuitions

4.2. General principles of kernel methods

4.3. General problems with kernel choices (kernel engineering)

4.4. Kernel versions of standard algorithms: examples of solvers

4.5. Kernels for text entities

4.6. Summary

4.7. Bibliography

Chapter 5. Topic-Based Generative Models for Text Information Access

5.1. Introduction

5.2. Topic-based models

5.3. Topic models

5.4. Term models

5.5. Similarity measures between documents

5.6. Conclusion

5.7. Appendix: topic model software

5.8. Bibliography

Chapter 6. Conditional ...