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Semi-supervised learning and domain adaptation in natural language processing /

Bibliographic Details
Main Author: Søgaard, Anders
Format: eBook
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, [2013]
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on human language technologies ; # 21.
Subjects:
Natural language processing (Computer science)
Supervised learning (Machine learning)
COMPUTERS / Natural Language Processing.
learning under bias
machine learning
natural language processing
semi-supervised learning
Online Access:Connect to the full text of this electronic book
Table of 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.