## Semisupervised Learning for Computational LinguisticsThe 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 offer |

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### Contents

1 | |

Selftraining and Cotraining | 13 |

Applications of SelfTraining and CoTraining | 31 |

Classification | 43 |

Mathematics for BoundaryOriented Methods | 67 |

BoundaryOriented Methods | 95 |

Clustering | 131 |

Generative Models | 153 |

### Other editions - View all

Semisupervised Learning for Computational Linguistics Steven Abney,Steven P. Abney Snippet view - 2008 |

### Common terms and phrases

algorithm arbitrary assignment Association for Computational averaging property boundary nodes centers centroid choose classifier clustering co-training column component Computational Linguistics conditional accuracy conditional independence consider constraint corresponding cross entropy data points decision boundary decision list defined diagonal direction discussed distance distribution dot product East Stroudsburg edge eigenvalues eigenvectors equal equation error example feasible set figure fixed flow Gaussian given gradient graph half-instances harmonic function Hence hyperplane i-th input iteration label propagation labeled instances labeled nodes Laplacian learner linear combination machine learning matrix maximizes measure methods minimizes Naive Bayes Natural Language negative neighbors objective function optimal orthonormal matrix pairs part-of-speech tagging particle perpendicular positive prediction predictor probability problem Proceedings Rayleigh quotient represents rule sample self-training semisupervised learning solution space target function training data transductive unlabeled data unsupervised learning update WordNet zero