This chapter involves a better understanding of deep structure in text and also how to deep parse text and use it in various NLP applications. Now, we are equipped with various NLP preprocessing steps. Let's move to some deeper aspect of the text. The structure of language is so complex that we can describe it by various layers of structural processing. In this chapter we will touch upon all these structures in text, differentiate between them, and provide you with enough details about the usage of one of these. We will talk about context-free grammar (CFG) and how it can be implemented with NLTK. We will also look at the various parsers and how we can use some of the existing parsing methods in NLTK. We will write a shallow parser in NLTK and will again talk about NER in the context of chunking. We will also provide details about some options that exist in NLTK to do deep structural analysis. We will also try to give you some real-world use cases of...
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