## Linguistic Structure PredictionA major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference |

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### Contents

1 | |

Making Predictions | 23 |

Learning Structure from Annotated Data | 69 |

Learning Structure from Incomplete Data | 109 |

Inference | 147 |

Numerical Optimization | 169 |

Experimentation | 181 |

Maximum Entropy | 199 |

Locally Normalized Conditional Models | 203 |

Bibliography | 209 |

Authors Biography | 241 |

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annotated approach approximate argmax axioms Bayesian calculate chapter Cited on page(s Computational Linguistics ComputationalLinguistics conditional models consider constraints context-free corresponds count ofi s.t. dataset decoding problem defined denote dependency parsing derivation Dirichlet discussed DP equations dynamic programming entropy factor feature functions feature vector figure Gibbs sampling given goal grammar graph graphical models hidden variable hyperarc hypergraph hyperpath Ifwe input iteration linear linguistic structure prediction locally normalized log-linear models logic program loss function machine learning Markov maximizing maximum likelihood estimation multinomial distributions named entity Natural Language Processing nonterminal notation Note null hypothesis objective function ofthe output parameters Pitman-Yor posterior predictors probabilistic probability Proceedings proof pw(Y random variable reverse value sample semiring sentence sequence labeling solving standard error statistic step tagging techniques theorem training data unsupervised learning update vertex weights word y∈Yx