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Neural Network Methods for Natural Language Processing

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Cover of 'Neural Network Methods for Natural Language Processing'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Learning Basics and Linear Models
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    Chapter 3 From Linear Models to Multi-layer Perceptrons
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    Chapter 4 Feed-forward Neural Networks
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    Chapter 5 Neural Network Training
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    Chapter 6 Features for Textual Data
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    Chapter 7 Case Studies of NLP Features
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    Chapter 8 From Textual Features to Inputs
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    Chapter 9 Language Modeling
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    Chapter 10 Pre-trained Word Representations
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    Chapter 11 Using Word Embeddings
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    Chapter 12 Case Study: A Feed-forward Architecture for Sentence Meaning Inference
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    Chapter 13 Ngram Detectors: Convolutional Neural Networks
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    Chapter 14 Recurrent Neural Networks: Modeling Sequences and Stacks
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    Chapter 15 Concrete Recurrent Neural Network Architectures
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    Chapter 16 Modeling with Recurrent Networks
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    Chapter 17 Conditioned Generation
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    Chapter 18 Modeling Trees with Recursive Neural Networks
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    Chapter 19 Structured Output Prediction
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    Chapter 20 Cascaded, Multi-task and Semi-supervised Learning
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    Chapter 21 Conclusion
Attention for Chapter 20: Cascaded, Multi-task and Semi-supervised Learning
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