Natural Language Processing With Python
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Natural Language Processing With Python
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Author : Steven Bird
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2009-06-12
Natural Language Processing With Python written by Steven Bird and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-12 with Computers categories.
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.
Natural Language Understanding With Python
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Author : Deborah A. Dahl
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30
Natural Language Understanding With Python written by Deborah A. Dahl and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
Build advanced NLU systems by utilizing NLP libraries such as NLTK, SpaCy, BERT, and OpenAI; ML libraries like Keras, scikit-learn, pandas, TensorFlow, and NumPy, along with visualization libraries such as Matplotlib and Seaborn. Purchase of the print Kindle book includes a free PDF eBook Key Features Master NLU concepts from basic text processing to advanced deep learning techniques Explore practical NLU applications like chatbots, sentiment analysis, and language translation Gain a deeper understanding of large language models like ChatGPT Book DescriptionNatural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.What you will learn Explore the uses and applications of different NLP techniques Understand practical data acquisition and system evaluation workflows Build cutting-edge and practical NLP applications to solve problems Master NLP development from selecting an application to deployment Optimize NLP application maintenance after deployment Build a strong foundation in neural networks and deep learning for NLU Who this book is for This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.
Natural Language Processing With Python
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Author : Frank Millstein
language : en
Publisher: Frank Millstein
Release Date : 2020-07-06
Natural Language Processing With Python written by Frank Millstein and has been published by Frank Millstein this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-06 with Computers categories.
Natural Language Processing With Python This book is a perfect beginner's guide to natural language processing. It is offering an easy to understand guide to implementing NLP techniques using Python. Natural language processing has been around for more than fifty years, but just recently with greater amounts of data present and better computational powers, it has gained a greater popularity. Given the importance of data, there is no wonder why natural language processing is on the rise. If you are interested in learning more, this book will serve as your best companion on this journey introducing you to this challenging, yet extremely engaging world of automatic manipulation of our human language. It covers all the basics you need to know before you dive deeper into NLP and solving more complex NLP tasks in Python. Here Is a Preview of What You’ll Learn Here… The main challenges of natural language processing The history of natural language processing How natural langauge processing actually works The main natural language processing applications Text preprocessing and noise removal Feature engineering and syntactic parsing Part of speech tagging and named entity extraction Topic modeling and word embedding Text classification problems Working with text data using NLTK Text summarization and sentiment analysis And much, much more... Get this book NOW and learn more about Natural Language Processing With Python!
Natural Language Processing With Python Cookbook
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Author : Krishna Bhavsar
language : en
Publisher:
Release Date : 2017-11-24
Natural Language Processing With Python Cookbook written by Krishna Bhavsar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-24 with Computers categories.
Learn the tricks and tips that will help you design Text Analytics solutionsAbout This Book* Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python* Use dictionaries to create your own named entities using this easy-to-follow guide* Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language ProcessingWho This Book Is ForThis book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended.What You Will Learn* Explore corpus management using internal and external corpora* Learn WordNet usage and a couple of simple application assignments using WordNet* Operate on raw text* Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more* Understand regular expressions for pattern matching* Learn to use and write your own POS taggers and grammars* Learn to evaluate your own trained models* Explore Deep Learning techniques in NLP* Generate Text from Nietzsche's writing using LSTM* Utilize the BABI dataset and LSTM to model episodesIn DetailNatural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora.This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK-the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques.By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python.Style and ApproachThis book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Natural Language Processing With Python And Spacy
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Author : Yuli Vasiliev
language : en
Publisher: No Starch Press
Release Date : 2020-05-12
Natural Language Processing With Python And Spacy written by Yuli Vasiliev and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-12 with Computers categories.
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.
Natural Language Processing With Python
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-16
Natural Language Processing With Python written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Computers categories.
Learn NLP with Python through practical exercises, advanced topics like transformers, and real-world projects such as chatbots and dashboards. A comprehensive guide for mastering NLP techniques. Key Features A comprehensive guide to processing, analyzing, and modeling human language with Python Real-world projects that reinforce NLP concepts, including chatbot design and sentiment analysis Foundational and advanced NLP techniques for practical applications in diverse domains Book DescriptionEmbark on a comprehensive journey to master natural language processing (NLP) with Python. Begin with foundational concepts like text preprocessing, tokenization, and key Python libraries such as NLTK, spaCy, and TextBlob. Explore the challenges of text data and gain hands-on experience in cleaning, tokenizing, and building basic NLP pipelines. Early chapters provide practical exercises to solidify your understanding of essential techniques. Advance to sophisticated topics like feature engineering using Bag of Words, TF-IDF, and embeddings like Word2Vec and BERT. Delve into language modeling with RNNs, syntax parsing, and sentiment analysis, learning to apply these techniques in real-world scenarios. Chapters on topic modeling and text summarization equip you to extract insights from data, while transformer-based models like BERT take your skills to the next level. Each concept is paired with Python-based examples, ensuring practical mastery. The final chapters focus on real-world projects, such as developing chatbots, sentiment analysis dashboards, and news aggregators. These hands-on applications challenge you to design, train, and deploy robust NLP solutions. With its structured approach and practical focus, this book equips you to confidently tackle real-world NLP challenges and innovate in the field.What you will learn Clean and preprocess text data using Python effectively Master tokenization techniques for words, sentences, and characters Build robust NLP pipelines with feature engineering methods Implement sentiment analysis with machine learning models Perform topic modeling using LDA, LSA, and other algorithms Develop chatbots and dashboards for real-world applications Who this book is for This book is ideal for students, researchers, and professionals in machine learning, data science, and artificial intelligence who want to master NLP. Beginners will benefit from the step-by-step introduction to text processing and feature engineering, while experienced practitioners can explore advanced topics like transformers and real-world projects. Basic knowledge of Python and familiarity with programming concepts are recommended to fully utilize the content. Enthusiasts with a passion for language technology will also find this guide valuable for building practical NLP applications.
Natural Language Processing With Python
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Author : Dr. Bharti Salunke
language : en
Publisher: Xoffencerpublication
Release Date : 2024-11-06
Natural Language Processing With Python written by Dr. Bharti Salunke and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-06 with Computers categories.
Natural Language Processing (NLP) is a rapidly evolving field within artificial intelligence that focuses on the interaction between computers and human languages. It is concerned with the ability of machines to read, understand, and generate human language in a way that is both meaningful and contextually relevant. The integration of NLP with Python has revolutionized this domain, as Python's simplicity, versatility, and extensive libraries make it an ideal tool for developing NLP applications. This abstract delves into the essential aspects of NLP using Python, exploring key concepts, tools, and techniques that enable machines to process and analyze large amounts of natural language data. At its core, NLP involves several fundamental tasks, including tokenization, part-of-speech tagging, named entity recognition, syntactic parsing, and sentiment analysis. Python, with its rich ecosystem of libraries such as NLTK, spaCy, and transformers, provides an accessible and robust framework for tackling these tasks. Tokenization, for instance, breaks down text into smaller units such as words or sentences, which forms the foundation for many NLP applications. Part-of-speech tagging assigns grammatical labels to words, while named entity recognition identifies specific entities like names, dates, or locations within the text. Syntactic parsing helps in understanding the grammatical structure of sentences, and sentiment analysis enables machines to determine the emotional tone of a piece of text. One of the significant advancements in NLP is the application of machine learning techniques to language processing. Python’s libraries such as scikit-learn, TensorFlow, and PyTorch offer powerful tools for training models that can predict and classify language data. Deep learning models, particularly those based on neural networks, have led to major breakthroughs in tasks like machine translation, speech recognition, and question answering. Pre-trained models like BERT and GPT, implemented through Python frameworks, have set new benchmarks in NLP, allowing developers to build more sophisticated and accurate systems with minimal training data.
Natural Language Processing Recipes
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Author : Akshay Kulkarni
language : en
Publisher: Apress
Release Date : 2019-01-29
Natural Language Processing Recipes written by Akshay Kulkarni and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Computers categories.
Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing. By using the recipes in thisbook, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. What You Will Learn Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural language generation problems Who This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing through coding exercises.
Natural Language Processing With Python Quick Start Guide
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Author : Nirant Kasliwal
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30
Natural Language Processing With Python Quick Start Guide written by Nirant Kasliwal and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Natural Language Processing In Action
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Author : Hannes Hapke
language : en
Publisher: Simon and Schuster
Release Date : 2019-03-16
Natural Language Processing In Action written by Hannes Hapke and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-16 with Computers categories.
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. 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 Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets 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 networks Sequence-to-sequence models and attention PART 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)