Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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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...