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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

By : Zhenya Antić
4.4 (18)
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

4.4 (18)
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)
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Building a basic Rasa chatbot

In this recipe, we will use a popular chatbot framework, Rasa, to build a default chatbot. In the coming recipes, we will make the chatbot better.

Rasa is an open source deep learning framework for building chatbots. It uses Keras and Tensorflow to implement the model. You can read more about the implementation here: https://blog.tensorflow.org/2020/12/how-rasa-open-source-gained-layers-of-flexibility-with-tensorflow-2x.html.

Getting ready

We will initialize the Rasa framework and use it to build and initialize a default Rasa chatbot and then we will explore its structure. If you haven't already, install the rasa package:

pip install rasa

How to do it…

After installing the rasa package, there are new commands available through the Rasa interface. We will use them to create a default chatbot. The steps for this recipe are as follows:

  1. On the command line, enter this:
    rasa init

    Rasa will start and will produce some colorful...

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