Kulkarni A., Shivananda A. - Natural Language Processing Recipes [2019, PDF, ENG] torrent

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Natural Language Processing Recipes

Год издания: 2019
Автор: Kulkarni A., Shivananda A.

Издательство: Apress
ISBN: 978-1-4842-4267-4
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 253

Описание: 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 this book, 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

Kulkarni A., Shivananda A. - Natural Language Processing Recipes [2021, PDF/EPUB, ENG]
[spoiler="Примеры страниц"]

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[spoiler="Оглавление"]
About the Authors xiii
About the Technical Reviewers xv
Acknowledgments xvii
Introduction xix
Chapter 1: Extracting the Data 1
Chapter 2: Exploring and Processing Text Data 37
Chapter 3: Converting Text to Features 67
Chapter 4: Advanced Natural Language Processing 97
Chapter 5: Implementing Industry Applications 129
Chapter 6: Deep Learning for NLP 185
Index 229
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