Book Image

Python Natural Language Processing Cookbook

By : Zhenya Antić
Book Image

Python Natural Language Processing Cookbook

By: Zhenya Antić

Overview of this book

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.
Table of Contents (10 chapters)

Building a basic chatbot with keyword matching

In this recipe, we will build a very basic chatbot that uses keywords to match inquiries and responses. This is based on the NLTK Chat class.

Getting ready

We will start by creating a new chat class and initializing it with pairs of responses. Then, we will run it on the command line. If you haven't already, you will need to install the nltk package:

pip install nltk

How to do it…

We will use the NLTK chat framework to create a simple chatbot. First, we will create the question-answer pairs and then create the Chat object. We will then use it to converse with the bot:

  1. Do the necessary imports:
    from nltk.chat.util import Chat, reflections
  2. Initialize the conversation pairs:
    pairs = [[r".*hi|hello|hey|what's up.*", 
             ["Hello, I am a simple chatbot. How are you?"]],
             [r&apos...