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

Chopra, Rohan, Godbole, Aniruddha M., Sadvilkar, Nipun

  • 出版商: Packt Publishing
  • 出版日期: 2020-08-13
  • 售價: $1,320
  • 貴賓價: 9.5$1,254
  • 語言: 英文
  • 頁數: 452
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800208421
  • ISBN-13: 9781800208421
  • 相關分類: Text-mining 文字探勘

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商品描述

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

Key Features

  • Get familiar with key natural language processing (NLP) concepts and terminology
  • Explore the functionalities and features of popular NLP tools
  • Learn how to use Python programming and third-party libraries to perform NLP tasks

Book Description

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

Who this book is for

This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.