Natural Language Processing Cookbook

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Natural Language Processing Cookbook

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Author : Rosario Moscato
language : en
Publisher: BPB Publications
Release Date : 2025-02-26

Natural Language Processing Cookbook written by Rosario Moscato and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-26 with Computers categories.

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

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.

Python Natural Language Processing Cookbook

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Author : Zhenya Antić
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-19

Python Natural Language Processing Cookbook written by Zhenya Antić 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 2021-03-19 with Computers categories.

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.

Python Natural Language Processing Cookbook

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Author : Zhenya Antić
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-13

Python Natural Language Processing Cookbook written by Zhenya Antić 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 2024-09-13 with Computers categories.

Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAI Key Features Leverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI models Use LLM-powered agents for custom tasks and real-world interactions Gain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionHarness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.What you will learn Understand fundamental NLP concepts along with their applications using examples in Python Classify text quickly and accurately with rule-based and supervised methods Train NER models and perform sentiment analysis to identify entities and emotions in text Explore topic modeling and text visualization to reveal themes and relationships within text Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks Use question-answering techniques to handle both open and closed domains Apply XAI techniques to better understand your model predictions Who this book is for This updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you’re looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

Natural Language Processing With Java And Lingpipe Cookbook

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Author : Breck Baldwin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-11-28

Natural Language Processing With Java And Lingpipe Cookbook written by Breck Baldwin 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 2014-11-28 with Computers categories.

This book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial.

Natural Language Processing

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Author : Raymond Lee
language : en
Publisher: Springer Nature
Release Date : 2025-04-16

Natural Language Processing written by Raymond Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-16 with Computers categories.

This textbook provides a contemporary and comprehensive overview of Natural Language Processing (NLP), covering fundamental concepts, core algorithms, and key applications such as AI chatbots, Large Language Models and Generative AI. Additionally, it includes seven step-by-step NLP workshops, totaling 14 hours, that offer hands-on practice with essential Python tools, including NLTK, spaCy, TensorFlow, Keras, Transformers, and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.

Natural Language Processing

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Author : Raymond S. T. Lee
language : en
Publisher: Springer Nature
Release Date : 2023-11-14

Natural Language Processing written by Raymond S. T. Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-14 with Computers categories.

This textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.

Python For Natural Language Processing

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Author : Pierre M. Nugues
language : en
Publisher: Springer Nature
Release Date : 2024-07-09

Python For Natural Language Processing written by Pierre M. Nugues and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-09 with Computers categories.

Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing. As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines. The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures.

Natural Language Processing And Information Systems

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Author : Elisabeth Métais
language : en
Publisher: Springer Nature
Release Date : 2020-06-17

Natural Language Processing And Information Systems written by Elisabeth Métais and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Computers categories.

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, held in Saarbrücken, Germany, in June 2020.* The 15 full papers and 10 short papers were carefully reviewed and selected from 68 submissions. The papers are organized in the following topical sections: semantic analysis; question answering and answer generation; classification; sentiment analysis; personality, affect and emotion; retrieval, conversational agents and multimodal analysis. *The conference was held virtually due to the COVID-19 pandemic.

Getting Started With Deep Learning For Natural Language Processing

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Author : Sunil Patel
language : en
Publisher: BPB Publications
Release Date : 2021-01-13

Getting Started With Deep Learning For Natural Language Processing written by Sunil Patel and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-13 with Computers categories.

Learn how to redesign NLP applications from scratch. KEY FEATURESÊÊ ¥ Get familiar with the basics of any Machine Learning or Deep Learning application. ¥ Understand how does preprocessing work in NLP pipeline. ¥ Use simple PyTorch snippets to create basic building blocks of the network commonly used inÊ NLP.Ê ¥ Learn how to build a complex NLP application. ¥ Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques. ÊÊ DESCRIPTIONÊ Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered. WHAT YOU WILL LEARNÊ ¥ Learn how to leveraging GPU for Deep Learning ¥ Learn how to use complex embedding models such as BERT ¥ Get familiar with the common NLP applications. ¥ Learn how to use GANs in NLP ¥ Learn how to process Speech data and implementing it in Speech applications Ê WHO THIS BOOK IS FORÊ This book is a must-read to everyone who wishes to start the career with Machine learning and Deep Learning. This book is also for those who want to use GPU for developing Deep Learning applications. TABLE OF CONTENTSÊÊ 1. Understanding the basics of learning Process 2. Text Processing Techniques 3. Representing Language Mathematically 4. Using RNN for NLP 5. Applying CNN In NLP Tasks 6. Accelerating NLP with Advanced Embeddings 7. Applying Deep Learning to NLP tasks 8. Application of Complex Architectures in NLP 9. Understanding Generative Networks 10. Techniques of Speech Processing 11. The Road Ahead