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Artificial Intelligence with Python

You're reading from   Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839219535
Length 618 pages
Edition 2nd Edition
Languages
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Authors (2):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
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Toc

Table of Contents (26) Chapters Close

Preface 1. Introduction to Artificial Intelligence 2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER 3. Machine Learning Pipelines 4. Feature Selection and Feature Engineering 5. Classification and Regression Using Supervised Learning 6. Predictive Analytics with Ensemble Learning 7. Detecting Patterns with Unsupervised Learning 8. Building Recommender Systems 9. Logic Programming 10. Heuristic Search Techniques 11. Genetic Algorithms and Genetic Programming 12. Artificial Intelligence on the Cloud 13. Building Games with Artificial Intelligence 14. Building a Speech Recognizer 15. Natural Language Processing 16. Chatbots 17. Sequential Data and Time Series Analysis 18. Image Recognition 19. Neural Networks 20. Deep Learning with Convolutional Neural Networks 21. Recurrent Neural Networks and Other Deep Learning Models 22. Creating Intelligent Agents with Reinforcement Learning 23. Artificial Intelligence and Big Data 24. Other Books You May Enjoy
25. Index

Converting words to their base forms using stemming

Working with text means working with a lot of variation. We must deal with different forms of the same word and enable the computer to understand that these different words have the same base form. For example, the word sing can appear in many forms, such as singer, singing, song, sung, and so on. This set of words share similar meanings. This process is known as stemming. Stemming is a way of producing morphological variants of a root/base word. Humans can easily identify these base forms and derive context.

When analyzing text, it's useful to extract these base forms. Doing so enables the extraction of useful statistics derived from the input text. Stemming is one way to achieve this. The goal of a stemmer is to reduce words from their different forms into a common base form. It is basically a heuristic process that cuts off the ends of words to extract their base forms. Let's see how to do it using NLTK...

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