Book Image

NLTK Essentials

By : Nitin Hardeniya
Book Image

NLTK Essentials

By: Nitin Hardeniya

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>
Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Shallow versus deep parsing


In deep or full parsing, typically, grammar concepts such as CFG, and probabilistic context-free grammar (PCFG), and a search strategy is used to give a complete syntactic structure to a sentence. Shallow parsing is the task of parsing a limited part of the syntactic information from the given text. While deep parsing is required for more complex NLP applications, such as dialogue systems and summarization, shallow parsing is more suited for information extraction and text mining varieties of applications. I will talk about these in the next few sections with more details about their pros and cons and how we can use them for our NLP application.