Natural Language Processing in Action : Understanding, Analyzing, and Generating Text with Python

Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. All examples are included in the open source `nlpia` package on python.org and github.com, complete with a conda environment and Dockerfile to help you get going quickly on any platform. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries--all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Authors Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production for profit and fun: contributing to social-benefit projects like smart guides for people with blindness and cognitive assistance for those with developmental challenges or suffering from information overload (don't we all?). SummaryNatural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyRecent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.About the BookNatural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.What's insideSome sentences in this book were written by NLP! Can you guess which ones?Working with Keras, TensorFlow, gensim, and scikit-learnRule-based and data-based NLPScalable pipelinesAbout the ReaderThis book requires a basic understanding of deep learning and intermediate Python skills.About the AuthorHobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production.Table of ContentsPART 1 - WORDY MACHINESPackets of thought (NLP overview)Build your vocabulary (word tokenization)Math with words (TF-IDF vectors)Finding meaning in word counts (semantic analysis)PART 2 - DEEPER LEARNING (NEURAL NETWORKS)Baby steps with neural networks (perceptrons and backpropagation)Reasoning with word vectors (Word2vec)Getting words in order with convolutional neural networks (CNNs)Loopy (recurrent) neural networks (RNNs)Improving retention with long short-term memory networksSequence-to-sequence models and attentionPART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES)Information extraction (named entity extraction and question answering)Getting chatty (dialog engines)Scaling up (optimization, parallelization, and batch processing) "Learn both the theory and practical skills needed to go beyond merely understanding the inner workings of NLP, and start creating your own algorithms or models." Dr. Arwen Griffioen, Zendesk Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries-all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. Inside: Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines This book/course requires a basic understanding of deep learning and intermediate Python skills. Hobson Lane , Cole Howard , and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Provides a great overview of current NLP tools in Python. I'll definitely be keeping this book on hand for my own NLP work. Highly recommended! Tony Mullen, Northeastern University-Seattle An intuitive guide to get you started with NLP. The book is full of programming examples that help you learn in a very pragmatic way. Tommaso Teofili, Adobe Systems NARRATED BY MARK THOMAS Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, automatically generated document summaries--all at a low cost. Now techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.What's Inside• Some sentences of this book were written by NLP! Can you guess which ones?• Working with Keras, TensorFlow, gensim, and scikit-learn• Rule-based and data-based NLP• Scalable pipelinesThis book requires a basic understanding of deep learning and intermediate Python skills. PART 1. WORDY MACHINES 1. Packets of thought (NLP overview) 2. Build your vocabulary (word tokenization) 3. Math with words (TF-IDF vectors) 4. Finding meaning in word counts (semantic analysis) PART 2. DEEPER LEARNING (NEURAL NETWORKS) 5. Baby steps with neural networks (perceptrons and backpropagation) 6. Reasoning with word vectors (Word2vec) 7. Getting words in order with convolutional neural networks (CNNs) 8. Loopy (recurrent) neural networks (RNNs) 9. Improving retention with long short-term memory networks 10. Sequence-to-sequence models and attention PART 3. GETTING REAL (REAL-WORLD NLP CHALLENGES) 11. Information extraction (named entity extraction and question answering) 12. Getting chatty (dialog engines) 13. Scaling up (optimization, parallelization, and batch processing) Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI! You'll start with a mental model of how a computer learns to read and interpret language. Then, you'll discover how to train a Python-based NLP machine to recognize patterns and extract information from text. As you explore the carefully-chosen examples, you'll expand your machine's knowledge and apply it to a range of challenges, from building a search engine that can find documents based on their meaning rather than merely keywords, to training a chatbot that uses deep learning to answer questions and participate in a conversation.