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Author Søgaard, Anders.
Title Semi-supervised learning and domain adaptation in natural language processing [electronic resource] / Anders Søgaard.
Publication Info. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013.
Location Call No. Status Notes
 Libraries Electronic Books  ELECTRONIC BOOKS-DDA    AVAIL. ONLINE
Description 1 online resource.
Series Synthesis digital library of engineering and computer science.
Synthesis lectures on human language technologies ; # 21. 1947-4059
Note Part of: Synthesis digital library of engineering and computer science.
Title from PDF t.p. (viewed on June 15, 2013).
Series from website.
Bibliography Includes bibliographical references (p. 81-92).
Contents 1. Introduction -- 1.1 Introduction -- 1.2 Learning under bias -- 1.3 Empirical evaluations --
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 --
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 --
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 --
5. Learning under unknown bias -- 5.1 Adversarial learning -- 5.2 Ensemble-based methods and meta-learning --
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 --
Bibliography -- Author's biography.
Indexed In: Compendex
INSPEC
Google scholar
Google book search
Reproduction Electronic reproduction. Perth, W.A. Available via World Wide Web.
Subject Natural language processing (Computer science)
Supervised learning (Machine learning)
natural language processing
machine learning
learning under bias
semi-supervised learning
Added Author Ebooks Corporation
Related To Print version: 9781608459858
ISBN 9781608459865 (electronic bk.)
1608459861 (electronic bk.)
9781608459858 (pbk.)
Standard No. 10.2200/S00497ED1V01Y201304HLT021 doi
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