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)

Creating question-answer pairs with Rasa

Now we will build on the simple chatbot that we built in the previous recipe and create new conversation pairs. Our bot will answer simple, frequently asked questions for a business, such as questions about hours, address, and so on.

Getting ready

We will continue using the bot we created in the previous recipe. Please follow the installation instructions specified there.

How to do it…

In order to create new question-answer pairs, we will modify the following files: domain.yml, nlu.yml, and rules.yml. The steps are as follows:

  1. Open the domain.yml file and in the section named intents, add an intent named hours. The section should now look like this:
    intents:
      - greet
      - goodbye
      - affirm
      - deny
      - mood_great
      - mood_unhappy
      - bot_challenge
      - hours
  2. Now we will create a new response for a question about hours. Edit the section...