Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

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.
Table of Contents (10 chapters)
Preface

1. Introduction to Natural Language Processing

Activity 1.01: Preprocessing of Raw Text

Solution

Let's perform preprocessing on a text corpus. To complete this activity, follow these steps:

  1. Open a Jupyter Notebook.
  2. Insert a new cell and add the following code to import the necessary libraries:
    from nltk import download
    download('stopwords')
    download('wordnet')
    nltk.download('punkt')
    download('averaged_perceptron_tagger')
    from nltk import word_tokenize
    from nltk.stem.wordnet import WordNetLemmatizer
    from nltk.corpus import stopwords
    from autocorrect import Speller
    from nltk.wsd import lesk
    from nltk.tokenize import sent_tokenize
    from nltk import stem, pos_tag
    import string
  3. Read the content of file.txt and store it in a variable named sentence. Insert a new cell and add the following code to implement this:
    #load the text file into variable called sentence
    sentence = open("../data/file.txt", 'r').read...