Text data management and analysis : (Record no. 874)
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| 000 -LEADER | |
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| fixed length control field | 08549nam a2200553 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 011452915031 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260609132937.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 160803s2016 nyua fob 001 0deng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781970001174 |
| Qualifying information | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781970001181 |
| Qualifying information | ePub |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781970001167 |
| Qualifying information | paperback |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781970001198 |
| Qualifying information | hardcover |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1145/2915031 |
| Source of number or code | doi |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)954593347 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | CaBNVSL |
| Language of cataloging | eng |
| Description conventions | rda |
| Transcribing agency | CaBNVSL |
| Modifying agency | CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D343 |
| Item number | Z427 2016 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.312 |
| Edition information | 23 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (CaBNVSL)swl00406771 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Zhai, ChengXiang., |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Text data management and analysis : |
| Remainder of title | a practical introduction to information retrieval and text mining / |
| Statement of responsibility, etc. | ChengXiang Zhai, Sean Massung. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | [New York] : |
| Name of producer, publisher, distributor, manufacturer | Association for Computing Machinery ; |
| Place of production, publication, distribution, manufacture | [San Rafael, California] : |
| Name of producer, publisher, distributor, manufacturer | Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 PDF (xx, 510 pages) : |
| Other physical details | illustrations. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | electronic |
| Source | isbdmedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | ACM books, |
| International Standard Serial Number | 2374-6777 ; |
| Volume/sequential designation | #12 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references and index. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE | |
| Terms governing access | Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Title from PDF title page (viewed on August 3, 2016). |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | System requirements: Adobe Acrobat Reader. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | The growth of "big data" created unprecedented opportunities to leverage computational and statistical approaches to turn raw data into actionable knowledge that can support various application tasks. This is especially true for the optimization of decision making in virtually all application domains such as health and medicine, security and safety, learning and education, scientific discovery, and business intelligence. Just as a microscope enables us to see things in the "micro world" and a telescope allows us to see things far away, one can imagine a "big data scope" would enable us to extend our perception ability to "see" useful hidden information and knowledge buried in the data, which can help make predictions and improve the optimality of a chosen decision. This book covers general computational techniques for managing and analyzing large amounts of text data that can help users manage and make use of text data in all kinds of applications. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Also available in print. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Natural language processing (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computational linguistics |
| General subdivision | Statistical methods. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Massung, Sean., |
| Relator term | author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| International Standard Book Number | 9781970001198 |
| -- | 9781970001167 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | ACM books ; |
| Volume/sequential designation | #12. |
| International Standard Serial Number | 2374-6777 |
| 856 48 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Abstract with links to full text |
| Uniform Resource Identifier | <a href="http://dx.doi.org/10.1145/2915031">http://dx.doi.org/10.1145/2915031</a> |
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