Natural Language Processing With Python And Spacy A Practical Introduction
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Sprache:Englisch
46,99 €
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
12.05.2020
Verlag
No Starch Press,USSeitenzahl
216
Maße (L/B/H)
23,5/18,1/1,7 cm
Gewicht
435 g
Sprache
Englisch
ISBN
978-1-71850-052-5
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 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:
"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.
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