Introduction To Natural Language Processing A Practical Guide For Beginners
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| Author | : Sowmya Vajjala |
| Publisher | : O'Reilly Media |
| Total Pages | : 455 |
| Release | : 2020-06-17 |
| Genre | : Computers |
| ISBN | : 149205402X |
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
| Author | : Sakil Ansari |
| Publisher | : White Falcon Publishing |
| Total Pages | : 0 |
| Release | : 2023-03-02 |
| Genre | : |
| ISBN | : 9781636408354 |
"Introduction to Natural Language Processing: A practical guide for beginners" is a book that provides an overview of the field of natural language processing (NLP) and its applications. It is intended for individuals with little to no experience in the area. It aims to provide a comprehensive introduction to the concepts and techniques used in NLP. The book is aimed at beginners and offers a practical guide for understanding and working with NLP techniques. It covers NLP fundamental concepts and methods, such as tokenization, stemming, lemmatization, and part-of-speech tagging. It also discusses more advanced topics such as sentiment analysis, text generation, and machine translation. The book uses Python programming language and provides examples to help readers practice and apply the concepts they learn. The book also includes real-world case studies using NLP to solve real-world problems. The book is written clearly and concisely, making it easy for beginners to understand. It provides a good foundation for those interested in pursuing a career in NLP or related fields such as machine learning, artificial intelligence, or data science. It is also helpful for professionals who want to understand NLP and its applications in their areas. Overall, "Introduction to Natural Language Processing: A practical guide for beginners" is an excellent resource for anyone interested in learning about NLP. Whether you are a student, researcher, or professional, this book provides a comprehensive introduction to the field of NLP. It will help you understand and apply the concepts and techniques of this exciting field.
| Author | : Bhargav Srinivasa-Desikan |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 298 |
| Release | : 2018-06-29 |
| Genre | : Computers |
| ISBN | : 1788837037 |
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
| Author | : Steven Bird |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 506 |
| Release | : 2009-06-12 |
| Genre | : Computers |
| ISBN | : 0596555717 |
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
| Author | : Yuli Vasiliev |
| Publisher | : No Starch Press |
| Total Pages | : 217 |
| Release | : 2020-04-28 |
| Genre | : Computers |
| ISBN | : 171850053X |
An introduction to natural language processing with Python using spaCy, a leading Python natural language processing library. Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You'll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You'll even learn how to transform statements into questions to keep a conversation going. You'll also learn how to: • Work with word vectors to mathematically find words with similar meanings (Chapter 5) • Identify patterns within data using spaCy's built-in displaCy visualizer (Chapter 7) • Automatically extract keywords from user input and store them in a relational database (Chapter 9) • Deploy a chatbot app to interact with users over the internet (Chapter 11) "Try This" sections in each chapter encourage you to practice what you've learned by expanding the book's example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications. By the end of the book, you'll be creating your own NLP applications with Python and spaCy.
| Author | : Michael Walker |
| Publisher | : |
| Total Pages | : 76 |
| Release | : 2017-08-04 |
| Genre | : |
| ISBN | : 9781724811745 |
***BUY NOW (Will soon return to 19.59) ******Free eBook for customers who purchase the print book from Amazon*** Are you thinking of learning more about Natural Language Processing (NLP)? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using ]NLP. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of Data Science and NLP. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction to Natural Language Processing What is Natural Language Processing Perspectivizing NLP: Areas of AI and Their Interdependencies Purpose of Natural Language Processing Text Manipulation Tokenization Stemming Lemmatization Normalization Accessing Text Corpora and Lexical Resources Processing Raw Text Categorizing and Tagging Words NLP Applications Text Classification Sentiment Classification Topic Modelling Question Answering Speech Recognition Machine Translation Word Representation Bag of Words One-Hot Encoding Word Vectors Representation Word2Vec and GloVe Learning to Classify Text Supervised Classification Decision Trees Naive Bayes Classifiers Maximum Entropy Classifiers Deep Learning for NLP What is Deep Learning Feed Forward Neural Networks Recurrent Neural Networks Gated Recurrent Unit Long Short Term Memory Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash NLP concepts and Fundamentals for Beginners from scratch, this book is for you. No need for any coding experience. Q: Does this book include everything I need to become a NLP expert? A: Unfortunately, no. This book is designed for readers taking their first steps in NLP and further learning will be required beyond this book to master all aspects of NLP. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/
| Author | : Ekaterina Kochmar |
| Publisher | : Simon and Schuster |
| Total Pages | : 454 |
| Release | : 2022-11-15 |
| Genre | : Computers |
| ISBN | : 1638350922 |
Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human. In Getting Started with Natural Language Processing you’ll learn about: Fundamental concepts and algorithms of NLP Useful Python libraries for NLP Building a search algorithm Extracting information from raw text Predicting sentiment of an input text Author profiling Topic labeling Named entity recognition Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you’re a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you. About the technology From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP! About the book Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you’ll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you’ve previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning! What's inside Fundamental concepts and algorithms of NLP Extracting information from raw text Useful Python libraries Topic labeling Building a search algorithm About the reader You’ll need basic Python skills. No experience with NLP required. About the author Ekaterina Kochmar is a lecturer at the Department of Computer Science of the University of Bath, where she is part of the AI research group. Table of Contents 1 Introduction 2 Your first NLP example 3 Introduction to information search 4 Information extraction 5 Author profiling as a machine-learning task 6 Linguistic feature engineering for author profiling 7 Your first sentiment analyzer using sentiment lexicons 8 Sentiment analysis with a data-driven approach 9 Topic analysis 10 Topic modeling 11 Named-entity recognition
| Author | : Rohan Chopra |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 451 |
| Release | : 2020-08-17 |
| Genre | : Computers |
| ISBN | : 1800200803 |
Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data Key FeaturesGet familiar with key natural language processing (NLP) concepts and terminologyExplore the functionalities and features of popular NLP toolsLearn how to use Python programming and third-party libraries to perform NLP tasksBook Description Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text. What you will learnObtain, verify, clean and transform text data into a correct format for useUse methods such as tokenization and stemming for text extractionDevelop a classifier to classify comments in Wikipedia articlesCollect data from open websites with the help of web scrapingTrain a model to detect topics in a set of documents using topic modelingDiscover techniques to represent text as word and document vectorsWho this book is for This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
| Author | : Dan Jurafsky |
| Publisher | : Pearson Education India |
| Total Pages | : 912 |
| Release | : 2000-09 |
| Genre | : |
| ISBN | : 9788131716724 |
| Author | : Jacob Eisenstein |
| Publisher | : MIT Press |
| Total Pages | : 535 |
| Release | : 2019-10-01 |
| Genre | : Computers |
| ISBN | : 0262042843 |
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
