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NLTK Essentials
Build cool NLP and machine learning applications using NLTK and other Python libraries If you are an NLP or machine learning enthusiast with some or no experience in text processing, then this book is for you. This book is also ideal for expert Python programmers who want to learn NLTK quickly. Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it’s becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool. You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text. By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.
194 pages, Kindle Edition
First published July 27, 2015
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Displaying 1 - 2 of 2 reviews
April 23, 2016
Could be great without errors.
July 4, 2017
A brief and concise introduction to NLP through the NLTK library.
It helps NLP enthusiasts to get a glimpse of the various aspects of text processing such as Tokenization, Normalization, Stemming and POS-tagging.
Frankly, I didn't find it suitable for the more sophisticated NLP tasks such as parsing and CFG.
One of its shortcomings -in my opinion- that it provides the reader with some things that are not in the core of NLP such as web crawling and other Python libraries such as NumPy, Scikit-Learn, and Matblotlib.
I think it will be sufficient if you just read the first 6 chapters from it
It helps NLP enthusiasts to get a glimpse of the various aspects of text processing such as Tokenization, Normalization, Stemming and POS-tagging.
Frankly, I didn't find it suitable for the more sophisticated NLP tasks such as parsing and CFG.
One of its shortcomings -in my opinion- that it provides the reader with some things that are not in the core of NLP such as web crawling and other Python libraries such as NumPy, Scikit-Learn, and Matblotlib.
I think it will be sufficient if you just read the first 6 chapters from it
Displaying 1 - 2 of 2 reviews