November 2016
Beginner to intermediate
687 pages
15h 31m
English
book
Natural Language Processing: Python and NLTK
by Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur
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Chapter 8. Using NLTK with Other Python Libraries
In this chapter, we will explore some of the backbone libraries of Python for machine learning and natural language processing. Until now, we have used NLTK, Scikit, and genism, which had very abstract functions, and were very specific to the task in hand. Most of statistical NLP is heavily based on the vector space model, which in turn depends on basic linear algebra covered by NumPy. Also many NLP tasks, such as POS or NER tagging, are really classifiers in disguise. Some of the libraries we will discuss are heavily used in all these tasks.
The idea behind this chapter is to give you a quick overview of some the most fundamental Python libraries. This will help us understand more than just the ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.