The Natural Language Processing Workshop:Confidently design and build your own NLP projects with this easy-to-understand practical guide


The Natural Language Processing Workshop:Confidently design and build your own NLP projects with this easy-to-understand practical guide Paperback – 17 Aug. 2020
by:Rohan Chopra , Aniruddha M. Godbole , Nipun Sadvilkar , Muzaffar Bashir Shah
pages 页数:452 pages
ISBN-10 书号:1800208421
ISBN-13 书号:9781800208421
Product Dimensions:19.05 x 2.59 x 23.5 cm
Publisher Finelybook 出版社:Packt Publishing; 2nd edition (17 Aug. 2020)
Language 语言:English

Book Description
Make NLP easy by:building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data
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 learn

Obtain, verify, clean and transform text data into a correct format for use
Use methods such as tokenization and stemming for text extraction
Develop a classifier to classify comments in Wikipedia articles
Collect data from open websites with the help of web scraping
Train a model to detect topics in a set of documents using topic modeling
Discover techniques to represent text as word and document vectors

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