Part I. Overview and background -- 1. Introduction -- 1.1 Functions of text information systems -- 1.2 Conceptual framework for text information systems -- 1.3 Organization of the book -- 1.4 How to use this book -- Bibliographic notes and further reading -- 2. Background -- 2.1 Basics of probability and statistics -- 2.2 Information theory -- 2.3 Machine learning -- Bibliographic notes and further reading -- Exercises -- 3. Text data understanding -- 3.1 History and state of the art in NLP -- 3.2 NLP and text information systems -- 3.3 Text representation -- 3.4 Statistical language models -- Bibliographic notes and further reading -- Exercises -- 4. MeTA: a unified toolkit for text data management and analysis -- 4.1 Design philosophy -- 4.2 Setting up MeTA -- 4.3 Architecture -- 4.4 Tokenization with MeTA -- 4.5 Related toolkits -- Exercises --

Part II. Text data access -- 5. Overview of text data access -- 5.1 Access mode: pull vs. push -- 5.2 Multimode interactive access -- 5.3 Text retrieval -- 5.4 Text retrieval vs. database retrieval -- 5.5 Document selection vs. document ranking -- Bibliographic notes and further reading -- Exercises -- 6. Retrieval models -- 6.1 Overview -- 6.2 Common form of a retrieval function -- 6.3 Vector space retrieval models -- 6.4 Probabilistic retrieval models -- Bibliographic notes and further reading -- Exercises -- 7. Feedback -- 7.1 Feedback in the vector space model -- 7.2 Feedback in language models -- Bibliographic notes and further reading -- Exercises -- 8. Sarch engine implementation -- 8.1 Tokenizer -- 8.2 Indexer -- 8.3 Scorer -- 8.4 Feedback implementation -- 8.5 Compression -- 8.6 Caching -- Bibliographic notes and further reading -- Exercises -- 9. Search engine evaluation -- 9.1 Introduction -- 9.2 Evaluation of set retrieval -- 9.3 Evaluation of a ranked list -- 9.4 Evaluation with multi-level judgements -- 9.5 Practical issues in evaluation -- Bibliographic notes and further reading -- Exercises -- 10. Web search -- 10.1 Web crawling -- 10.2 Web indexing -- 10.3 Link analysis -- 10.4 Learning to rank -- 10.5 The future of web search -- Bibliographic notes and further reading -- Exercises -- 11. Recommender systems -- 11.1 Content-based recommendation -- 11.2 Collaborative filtering -- 11.3 Evaluation of recommender systems -- Bibliographic notes and further reading -- Exercises --

Part III. Text data analysis -- 12. Overview of text data analysis -- 12.1 Motivation: applications of text data analysis -- 12.2 Text vs. non-text data: humans as subjective sensors -- 12.3 Landscape of text mining tasks -- 13. Word association mining -- 13.1 General idea of word association mining -- 13.2 Discovery of paradigmatic relations -- 13.3 Discovery of syntagmatic relations -- 13.4 Evaluation of word association mining -- Bibliographic notes and further reading -- Exercises -- 14. Text clustering -- 14.1 Overview of clustering techniques -- 14.2 Document clustering -- 14.3 Term clustering -- 14.4 Evaluation of text clustering -- Bibliographic notes and further reading -- Exercises -- 15. Text categorization -- 15.1 Introduction -- 15.2 Overview of text categorization methods -- 15.3 Text categorization problem -- 15.4 Features for text categorization -- 15.5 Classification algorithms -- 15.6 Evaluation of text categorization -- Bibliographic notes and further reading -- Exercises -- 16. Text summarization -- 16.1 Overview of text summarization techniques -- 16.2 Extractive text summarization -- 16.3 Abstractive text summarization -- 16.4 Evaluation of text summarization -- 16.5 Applications of text summarization -- Bibliographic notes and further reading -- Exercises -- 17. Topic analysis -- 17.1 Topics as terms -- 17.2 Topics as word distributions -- 17.3 Mining one topic from text -- 17.4 Probabilistic latent semantic analysis -- 17.5 Extension of PLSA and latent Dirichlet allocation -- 17.6 Evaluating topic analysis -- 17.7 Summary of topic models -- Bibliographic notes and further reading -- Exercises -- 18. Opinion mining and sentiment analysis -- 18.1 Sentiment classification -- 18.2 Ordinal regression -- 18.3 Latent aspect rating analysis -- 18.4 Evaluation of opinion mining and sentiment analysis -- Bibliographic notes and further reading -- Exercises -- 19. Joint analysis of text and structured data -- 19.1 Introduction -- 19.2 Contextual text mining -- 19.3 Contextual probabilistic latent semantic analysis -- 19.4 Topic analysis with social networks as context -- 19.5 Topic analysis with time series context -- 19.6 Summary -- Bibliographic notes and further reading -- Exercises --

Part IV. Unified text data management analysis system -- 20. Toward a unified system for text management and analysis -- 20.1 Text analysis operators -- 20.2 System architecture -- 20.3 MeTA as a unified system --

Appendix A. Bayesian statistics -- Binomial estimation and the beta distribution -- Pseudo counts, smoothing, and setting hyperparameters -- Generalizing to a multinomial distribution -- The Dirichlet distribution -- Bayesian estimate of multinomial parameters -- Conclusion -- Appendix B. Expectation- maximization -- A simple mixture Unigram language model -- Maximum likelihood estimation -- Incomplete vs. complete data -- A lower bound of likelihood -- The general procedure of EM -- Appendix C. KL-divergence and Dirichlet prior smoothing -- Using KL-divergence for retrieval -- Using Dirichlet prior smoothing -- Computing the query model p(w [theta]q) -- References -- Index -- Authors' biographies.