| 000 -LEADER |
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| fixed length control field |
08224nam a22004577a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
APU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221031122702.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220307t20162016nyua b 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781970001174 (pdf) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781970001181 (epub) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Cancelled/invalid ISBN |
9781970001167 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Cancelled/invalid ISBN |
9781970001198 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)954593347 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(CaBNVSL)swl00406771 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Transcribing agency |
APU |
| Modifying agency |
SF |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.D343 |
| Item number |
Z42 2016eb |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.312 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Zhai, ChengXiang., |
| 9 (RLIN) |
47408 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Text data management and analysis : |
| Remainder of title |
a practical introduction to information retrieval and text mining |
| Medium |
[electronic resources] / |
| Statement of responsibility, etc |
ChengXiang Zhai, Sean Massung. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
[New York, NY] : |
| Name of publisher, distributor, etc |
ACM Books ; |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
[San Rafael, California] : |
| Name of publisher, distributor, etc |
Morgan & Claypool, |
| Date of publication, distribution, etc |
c2016. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resources (xx, 510 pages) : |
| Other physical details |
illustrations. |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
ACM books, |
| International Standard Serial Number |
2374-6777 ; |
| Volume number/sequential designation |
#12 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 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. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 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. |
| 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Natural language processing (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational linguistics |
| General subdivision |
Statistical methods. |
| 9 (RLIN) |
47409 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Massung, Sean., |
| 9 (RLIN) |
47410 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
ACM books ; |
| Volume number/sequential designation |
#12. |
| 9 (RLIN) |
47379 |
| 856 48 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://dl-acm-org.ezproxy.apu.edu.my/doi/book/10.1145/2915031 |
| Public note |
Available in ACM Digital Library. Requires Log In to view full text. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
E-Book |
