Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language

Table of Contents......Page 5
About the Author......Page 8
About the Technical Reviewer......Page 9
Acknowledgments......Page 10
Introduction......Page 11
Chapter 1: What Is Natural Language Processing?......Page 12
 The History of Natural Language Processing......Page 13
   TensorFlow......Page 15
   Keras......Page 18
   Theano......Page 19
   Word Embeddings......Page 21
   Language Modeling Tasks Involving RNNs......Page 22
 Summary......Page 23
 Multilayer Perceptrons and Recurrent Neural Networks......Page 24
  Toy Example 1: Modeling Stock Returns with the MLP Model......Page 26
   Learning Rate......Page 31
  Vanishing Gradients and Why ReLU Helps to Prevent Them......Page 38
  Loss Functions and Backpropagation......Page 40
  Recurrent Neural Networks and Long Short-Term Memory......Page 41
  Toy Example 2: Modeling Stock Returns with the RNN Model......Page 43
  Toy Example 3: Modeling Stock Returns with the LSTM Model......Page 51
 Summary......Page 52
Chapter 3: Working with  Raw Text......Page 54
 Tokenization and Stop Words......Page 55
 The Bag-of-Words Model (BoW)......Page 61
  CountVectorizer......Page 62
  Example Problem 1: Spam Detection......Page 64
  Term Frequency Inverse Document Frequency......Page 68
  Example Problem 2: Classifying Movie Reviews......Page 73
 Summary......Page 85
 Topic Model and Latent Dirichlet Allocation (LDA)......Page 87
  Topic Modeling with LDA on Movie Review Data......Page 91
 Non-Negative Matrix Factorization (NMF)......Page 96
 Word2Vec......Page 100
  Example Problem 4.2: Training a Word Embedding (Skip-Gram)......Page 104
 Continuous Bag-of-Words (CBoW)......Page 113
  Example Problem 4.2: Training a Word Embedding (CBoW)......Page 115
 Global Vectors for Word Representation (GloVe)......Page 116
  Example Problem 4.4: Using Trained Word Embeddings with LSTMs......Page 121
 Paragraph2Vec: Distributed Memory of Paragraph Vectors (PV-DM)......Page 125
  Example Problem 4.5: Paragraph2Vec Example with Movie Review Data......Page 126
 Summary......Page 128
Chapter 5: Text Generation, Machine Translation, and Other Recurrent Language Modeling Tasks......Page 130
 Text Generation with LSTMs......Page 131
  Bidirectional RNNs (BRNN)......Page 135
 Creating a Name Entity Recognition Tagger......Page 137
 Sequence-to-Sequence Models (Seq2Seq)......Page 142
 Question and Answer with Neural Network Models......Page 143
 Summary......Page 150
 Conclusion and Final Statements......Page 151
Index......Page 153