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

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. __Applied Natural Language Processing with Python__starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. **What You Will Learn** * Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim * Manipulate and preprocess raw text data in formats such as .txt and .pdf * Strengthen your skills in data science by learning both the theory and the application of various algorithms **Who This Book Is For** You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content. Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content. Contents......Page 3 Intro......Page 6 Natural Language Processing......Page 7 History of NLP......Page 8 Review of Machine & Deep Learning......Page 10 Summary......Page 18 Multilayer Perceptrons & RNNs......Page 19 Summary......Page 47 Raw Text......Page 49 Tokenization & Stop Words......Page 50 Bag-of-Words Model (BoW)......Page 56 Summary......Page 80 Topic Model & Latent Dirichlet Allocation (LDA)......Page 82 Non-Negative Matrix Factorization (NMF)......Page 91 Word2Vec......Page 95 Continuous Bag-of-Words (CBoW)......Page 108 Global Vectors for Word Representation (GloVe)......Page 111 Paragraph2Vec - Distributed Memory of Paragraph Vectors (PV-DM)......Page 120 Summary......Page 123 Text Generation, Machine Translation, & other Recurrent Language Modeling Tasks......Page 125 Text Generation with LSTMs......Page 126 Name Entity Recognition Tagger......Page 132 Sequence-to-Sequence Models (Seq2Seq)......Page 137 Question & Answer with NN Models......Page 138 Summary......Page 145 Conclusion & Final Statements......Page 146 Index......Page 148