This chapter was intended to expose the reader to some of the most useful NLP pre-processing steps of tagging. We have talked about the Part of Speech problem in general, including the significance of POS in the context of NLP. We also discussed the different ways we can use a pre-trained POS tagger in NLTK, how simple it is to use, and how to create wonderful applications. We then talked about all the available POS tagging options, like N-gram tagging, Regex based tagging, etc. We have developed a mix of these taggers that can be built for domain specific corpuses. We briefly talked about how a typical pre-trained tagger is built. We discussed the possible approaches to address tagging problems. We also talked about NER taggers, and how it works with NLTK. I think if, by the end of this chapter, the user understands the importance of POS and NER in general in the context of NLP, as well as how to run the snippet of codes using NLTK, I will consider this chapter successful. But the...
NLTK Essentials
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NLTK Essentials
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Overview of this book
<p>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.</p>
<p>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.</p>
<p>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.</p>
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Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to Natural Language Processing
Text Wrangling and Cleansing
Part of Speech Tagging
Parsing Structure in Text
NLP Applications
Text Classification
Web Crawling
Using NLTK with Other Python Libraries
Social Media Mining in Python
Text Mining at Scale
Index
Customer Reviews