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)

Chapter 7: Building Chatbots

In this chapter, we will build chatbots using two different frameworks, the nltk.chat package and the Rasa framework. The first recipe talks about the nltk.chat package, where we build a simple keyword matching chatbot, and the rest of the chapter is devoted to Rasa. Rasa is a complex framework that allows the creation of very sophisticated chatbots, and we will have a basic introduction to it. We will build a default bot using Rasa, and then we will modify it to do simple interactions.

Here is the list of this chapter's recipes:

  • Building a basic chatbot with keyword matching
  • Building a basic Rasa chatbot
  • Creating question-answer pairs with Rasa
  • Creating and visualizing conversation paths with Rasa
  • Creating actions for the Rasa chatbot