## Neural Network Methods in Natural Language ProcessingMorgan & Claypool Publishers, 17 Nis 2017 - 309 sayfa
The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning. |

### İçindekiler

Introduction | |

Learning Basics and Linear Models | |

From Linear Models to Multilayer Perceptrons | |

Neural Network Training | |

Features for Textual Data | |

Case Studies of NLP Features | |

Pretrained Word Representations | |

Using Word Embeddings | |

A Feedforward Architecture for Sentence Meaning Inference | |

Convolutional Neural Networks | |

Modeling Sequences and Stacks | |

Modeling with Recurrent Networks | |

Modeling Trees with Recursive Neural Networks | |

Structured Output Prediction | |