## Statistical Language LearningEugene Charniak breaks new ground in artificial intelligence research by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background. New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques -- word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation -- along with the underlying mathematics and chapter exercises. Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics." "Language, Speech, and Communication" |

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#### Review: Statistical Language Learning

User Review - Leif - GoodreadsI had this book sitting on my shelf for several months without realizing that it's a wonderfully brief, thorough, and understandable introduction to several important natural language processing techniques. An excellent introduction for computer science folks looking to get into NLP. Read full review

### Contents

The Standard Model | 1 |

Statistical Models apd the Entropy of English | 21 |

Hidden Markov Models and Two Applications | 39 |

Algorithms for Hidden Markov Models | 53 |

Probabilistic ContextFree Grammars | 75 |

The Mathematics of PCFGs | 87 |

Learning Probabilistic Grammars | 103 |

Syntactic Disambiguation | 119 |

Word Senses and Their Disambiguation | 147 |

163 | |

Glossary | 165 |