The Nlp Cookbook

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The NLP Cookbook

Author: Fran Burgess
language: en
Publisher: Crown House Publishing
Release Date: 2011-11-16
The NLP Cookbook is a veritable smorgasbord of NLP and related techniques gleaned from some of the greatest names in the field and adapted to provide an encyclopaedic resource for all therapists, coaches, change agents or health professionals.Fran Burgess uses the metaphor of cooking to describe the process of bringing together the best ingredients in NLP and selecting them carefully in order to produce some mouth watering results. The recipes are grouped into sections depending on their purpose. Quite a few focus on how to shift state, with some of these targeting specific states like acceptance and anxiety. These are followed by recipes that seek to develop behaviours and skills, and others that address beliefs and identity. There is then a wide range to choose from which deal with goals, relationships and the process of change.The beauty is that most of them can be used time and again for different circumstances and contexts, so they never wear out. Each recipe is prefaced by an introduction, giving you some background to its source and evolution. You are provided with its ingredients, should you be interested in its engineering, plus timings and materials required, and if it is suitable for working solo, or with a partner. Novice cooks can follow the recipes slavishly whereas those with more experience can adapt a recipe, adding a little something here, removing a little something there. This is not magic. They understand the chemistry that underpins the cooking process. They know what happens when you put this with that, now or later.
Natural Language Processing Cookbook

DESCRIPTION Natural language processing (NLP) is revolutionizing how machines understand and interact with human language, creating powerful applications from chatbots to text analytics. This provides a practical, hands-on approach to mastering these technologies, making complex NLP concepts accessible through step-by-step recipes and real-world examples. This book walks you through the world of teaching computers to understand human language, starting with the basics and building up to advanced techniques. You will learn how to break down text into meaningful pieces, use Python programming to handle text data, and clean up messy text for analysis. The book shows you how computers can understand the meaning behind words using methods like word embeddings and BERT. You will discover how to identify parts of speech and recognize names of people and places in text, and how to sort text into different categories using ML. Advanced topics include finding hidden themes in document collections, building chatbots that can have conversations, and creating visual representations of text data. Throughout the book, practical Python examples help you implement these techniques while considering how to evaluate and deploy real-world NLP systems. By the time you complete this book, you will possess the technical proficiency to implement complete NLP pipelines from preprocessing to deployment. The recipe-based approach ensures you can immediately apply these techniques to solve real business problems. KEY FEATURES ● Step-by-step approach for each technique, with practical examples to fully master NLP. ● Add value to your data by mastering the most important NLP techniques. ● Readily usable recipes for implementing basic tasks like data cleaning and tokenization to more complicated neural network implementations. WHAT YOU WILL LEARN ● Preprocess and clean text for accurate NLP model performance. ● Apply ML techniques for text classification tasks. ● Extract key insights using semantic analysis and embeddings. ● Develop and fine-tune topic modeling algorithms. ● Build intelligent chatbots with dialogue management and intent detection. ● Visualize text data with word clouds and entity graphs. WHO THIS BOOK IS FOR This book is ideal for data scientists, programmers, business analysts, and students with basic Python knowledge who want to build practical NLP skills. Whether you are an AI enthusiast looking to enter the field or a professional seeking to add language processing capabilities to your toolkit, you will find actionable recipes that bridge theory and application. TABLE OF CONTENTS 1. Getting Started with NLP 2. Python for Text Processing 3. Text Processing and Cleaning 4. Semantic Representation 5. Part-of-speech Tagging and Named Entity Recognition 6. Text Classification 7. Advanced Techniques for Topic Modeling 8. Building a Chatbot 9. Text Data Visualization Techniques 10. Conclusion and Takeaways
Python Natural Language Processing Cookbook

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualization Key Features Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensim Implement common and not-so-common linguistic processing tasks using Python libraries Overcome the common challenges faced while implementing NLP pipelines Book DescriptionPython is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.What you will learn Become well-versed with basic and advanced NLP techniques in Python Represent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddings Perform text classification using different methods, including SVMs and LSTMs Explore different techniques for topic modeling such as K-means, LDA, NMF, and BERT Work with visualization techniques such as NER and word clouds for different NLP tools Build a basic chatbot using NLTK and Rasa Extract information from text using regular expression techniques and statistical and deep learning tools Who this book is for This book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects.