Deep Learning for Natural Language Processing: Creating Neural Networks with Python

Description

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP. Table of Contents About the Authors About the Technical Reviewer Acknowledgments Introduction Chapter 1: Introduction to Natural Language Processing and Deep Learning Python Packages NumPy Pandas SciPy Introduction to Natural Language Processing What Is Natural Language Processing? Good Enough, But What Is the Big Deal? What Makes Natural Language Processing Difficult? Ambiguity at Word Level Ambiguity at Sentence Level Ambiguity at Meaning Level What Do We Want to Achieve Through Natural Language Processing? Common Terms Associated with Language Processing Natural Language Processing Libraries NLTK TextBlob SpaCy Gensim Pattern Stanford CoreNLP Getting Started with NLP Text Search Using Regular Expressions Text to List Preprocessing the Text Accessing Text from the Web Removal of Stopwords Counter Vectorization TF-IDF Score Text Classifier Introduction to Deep Learning How Deep Is “Deep”? What Are Neural Networks? Basic Structure of Neural Networks Types of Neural Networks Feedforward Neural Networks Convolutional Neural Networks Recurrent Neural Networks Encoder-Decoder Networks Recursive Neural Networks Multilayer Perceptrons Stochastic Gradient Descent Backpropagation Deep Learning Libraries Theano Theano Installation Theano Examples TensorFlow Data Flow Graphs TensorFlow Installation TensorFlow Examples Keras Keras Installation Keras Principles Keras Examples Next Steps Chapter 2: Word Vector Representations Introduction to Word Embedding Neural Language Model Word2vec Skip-Gram Model Model Components: Architecture Model Components: Hidden Layer Model Components: Output Layer CBOW Model Subsampling Frequent Words Negative Sampling Word2vec Code Skip-Gram Code CBOW Code Next Steps Chapter 3: Unfolding Recurrent Neural Networks Recurrent Neural Networks What Is Recurrence? Differences Between Feedforward and Recurrent Neural Networks Recurrent Neural Network Basics Natural Language Processing and Recurrent Neural Networks RNNs Mechanism Training RNNs Meta Meaning of Hidden State of RNN Tuning RNNs Long Short-Term Memory Networks Components of LSTM How LSTM Helps to Reduce the Vanishing Gradient Problem Understanding GRUs Limitations of LSTMs Sequence-to-Sequence Models What Is It? Bidirectional Encoder Stacked Bidirectional Encoder Decoder Advanced Sequence-to-Sequence Models Attention Scoring Teacher Forcing Peeking Sequence-to-Sequence Use Case Next Steps Chapter 4: Developing a Chatbot Introduction to Chatbot Origin of Chatbots But How Does a Chatbot Work, Anyway? Why Are Chatbots Such a Big Opportunity? Building a Chatbot Can Sound Intimidating. Is It Actually? Conversational Bot Chatbot: Automatic Text Generation Next Steps Chapter 5: Research Paper Implementation: Sentiment Classification Self-Attentive Sentence Embedding Proposed Approach Model Penalization Term Visualization General Case Sentiment Analysis Case Research Findings Implementing Sentiment Classification Sentiment Classification Code Model Results TensorBoard Model Accuracy and Cost Case 1 Case 2 Scope for Improvement Next Steps Index