Semisupervised Learning for Computational Linguistics
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A01=Steven Abney
advanced semisupervised NLP applications
Ambiguity Resolution
Author_Steven Abney
bayes
boundaries
Category=CFX
classifier
Computational Linguistics
Conditional Accuracy
data
decision
Decision Boundary
eq_bestseller
eq_dictionaries-language-reference
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Error Regions
expectation maximization algorithm
Fixed Point Equation
Harmonic Functions
High Confidence Predictions
instance
Labeled Training Data
Linear Separator
Log Likelihood Ratio
naive
Naive Bayes Classifier
natural language processing methods
part-of
Part-of Speech Tagging
Pseudo Relevance Feedback
Rayleigh Quotient
Semisupervised Learning
Semisupervised Learning Methods
spectral clustering approaches
supervised learning techniques
support vector machines SVMs
Target Function
training
Training Data
Transductive Learner
Transductive SVM
unlabeled
Unlabeled Data
Unlabeled Instances
Unlabeled Node
Unlabeled Training Data
unsupervised classification
Product details
- ISBN 9781584885597
- Weight: 612g
- Dimensions: 156 x 234mm
- Publication Date: 17 Sep 2007
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offers self-contained coverage of semisupervised methods that includes background material on supervised and unsupervised learning.
The book presents a brief history of semisupervised learning and its place in the spectrum of learning methods before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, support vector machines (SVMs), and the null-category noise model. In addition, the book covers clustering, the expectation-maximization (EM) algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation as well as a detailed discussion of spectral methods.
Taking an intuitive approach to the material, this lucid book facilitates the application of semisupervised learning methods to natural language processing and provides the framework and motivation for a more systematic study of machine learning.
Abney, Steven
