| 000 -LEADER |
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| fixed length control field |
05956nam a2200649 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
6813752 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152910.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
130615s2013 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781608459865 (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781608459858 (pbk.) |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00497ED1V01Y201304HLT021 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaBNVSL)swl00402475 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)848841958 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.N38 |
| Item number |
S647 2013 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.35 |
| Edition number |
23 |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
|
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
MoCl |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Søgaard, Anders. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Semi-supervised learning and domain adaptation in natural language processing |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc. |
Anders Søgaard. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : |
| Name of publisher, distributor, etc. |
Morgan & Claypool, |
| Date of publication, distribution, etc. |
c2013. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 electronic text (x, 93 p.) : |
| Other physical details |
ill., digital file. |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on human language technologies, |
| International Standard Serial Number |
1947-4059 ; |
| Volume/sequential designation |
# 21 |
| 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. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Series from website. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (p. 81-92). |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1. Introduction -- 1.1 Introduction -- 1.2 Learning under bias -- 1.3 Empirical evaluations -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Supervised and unsupervised prediction -- 2.1 Standard assumptions in supervised learning -- 2.1.1 How to check whether the assumptions hold -- 2.2 Nearest neighbor -- 2.3 Naive Bayes -- 2.4 Perceptron -- 2.4.1 Large-margin methods -- 2.5 Comparisons of classification algorithms -- 2.6 Learning from weighted data -- 2.6.1 Weighted k-nearest neighbor -- 2.6.2 Weighted naive Bayes -- 2.6.3 Weighted perceptron -- 2.6.4 Weighted large-margin learning -- 2.7 Clustering algorithms -- 2.7.1 Hierarchical clustering -- 2.7.2 k-means -- 2.7.3 Expectation maximization -- 2.7.4 Evaluating clustering algorithms -- 2.8 Part-of-speech tagging -- 2.9 Dependency parsing -- 2.9.1 Transition-based dependency parsing -- 2.9.2 Graph-based dependency parsing -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Semi-supervised learning -- 3.1 Wrapper methods -- 3.1.1 Self-training -- 3.1.2 Co-training -- 3.1.3 Tri-training -- 3.1.4 Soft self-training, EM and co-EM -- 3.2 Clusters-as-features -- 3.3 Semi-supervised nearest neighbor -- 3.3.1 Label propagation -- 3.3.2 Semi-supervised nearest neighbor editing -- 3.3.3 Semi-supervised condensed nearest neighbor -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4. Learning under bias -- 4.1 Semi-supervised learning as transfer learning -- 4.2 Transferring data -- 4.2.1 Outlier detection -- 4.2.2 Importance weighting -- 4.3 Transferring features -- 4.3.1 Changing feature representation to minimize divergence -- 4.3.2 Structural correspondence learning -- 4.4 Transferring parameters -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Learning under unknown bias -- 5.1 Adversarial learning -- 5.2 Ensemble-based methods and meta-learning -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Evaluating under bias -- 6.1 What is language? -- 6.2 Significance across corpora -- 6.3 Meta-analysis -- 6.4 Performance and data characteristics -- 6.5 Down-stream evaluation -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Bibliography -- Author's biography. |
| 506 1# - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF t.p. (viewed on June 15, 2013). |
| 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 |
Supervised learning (Machine learning) |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
natural language processing |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
machine learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
learning under bias |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
semi-supervised learning |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781608459858 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on human language technologies ; |
| Volume/sequential designation |
# 21. |
| International Standard Serial Number |
1947-4059 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=6813752 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to full text |
| Uniform Resource Identifier |
http://dx.doi.org/10.2200/S00497ED1V01Y201304HLT021 |
