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Natural Language Processing: Python and NLTK

You're reading from Natural Language Processing: Python and NLTK Learn to build expert NLP and machine learning projects using NLTK and other Python libraries

Product type Course

Published in Nov 2016

Publisher Packt

ISBN-13 9781787285101

Length 702 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Mobile Application Development

Authors (5):

Iti Mathur

Jacob Perkins

Deepti Chopra

Nitin Hardeniya

Nisheeth Joshi

+1 more

View More author details

Table of Contents (6) Chapters

Preface

1. Module 1

2. Module 2 FREE CHAPTER

3. Module 3

B. Bibliography

Index

Preface

NLTK is one of the most popular and widely used library in the natural language processing (NLP) community. The beauty of NLTK lies in its simplicity, where most of the complex NLP tasks can be implemented using a few lines of code. Start off by learning how to tokenize text into component words. Explore and make use of the WordNet language dictionary. Learn how and when to stem or lemmatize words. Discover various ways to replace words and perform spelling correction. Create your own custom text corpora and corpus readers, including a MongoDB backed corpus. Use part-of-speech taggers to annotate words with their parts of speech. Create and transform chunked phrase trees using partial parsing. Dig into feature extraction for text classification and sentiment analysis. Learn how to do parallel and distributed text processing, and to store word distributions in Redis.

This learning path will teach you all that and more, in a hands-on learn-by-doing manner. Become an expert in using NLTK for Natural Language Processing with this useful companion.

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Authors (5)

Iti Mathur

Iti Mathur

Iti Mathur is an Assistant Professor at Banasthali University. Her areas of interest are computational semantics and ontological engineering. Besides this, she is also involved in the development of MT engines for English to Indian languages. She is one of the experts empaneled with TDIL program, Department of Electronics and Information Technology (DeitY), Govt. of India, a premier organization that oversees Language Technology Funding and Research in India. She has several publications in various journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.

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Deepti Chopra

Deepti Chopra

Deepti Chopra is an Assistant Professor at Banasthali University. Her primary area of research is computational linguistics, Natural Language Processing, and artificial intelligence. She is also involved in the development of MT engines for English to Indian languages. She has several publications in various journals and conferences and also serves on the program committees of several conferences and journals.

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Nitin Hardeniya

Nitin Hardeniya

Nitin Hardeniya is a data scientist with more than 4 years of experience working with companies such as Fidelity, Groupon, and [24]7-inc. He has worked on a variety of business problems across different domains. He holds a master's degree in computational linguistics from IIIT-H. He is the author of 5 patents in the field of customer experience. He is passionate about language processing and large unstructured data. He has been using Python for almost 5 years in his day-to-day work. He believes that Python could be a single-point solution to most of the problems related to data science. He has put on his hacker's hat to write this book and has tried to give you an introduction to all the sophisticated tools related to NLP and machine learning in a very simplified form. In this book, he has also provided a workaround using some of the amazing capabilities of Python libraries, such as NLTK, scikit-learn, pandas, and NumPy.

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Jacob Perkins

Jacob Perkins

Jacob Perkins is the cofounder and CTO of Weotta, a local search company. Weotta uses NLP and machine learning to create powerful and easy-to-use natural language search for what to do and where to go. He is the author of Python Text Processing with NLTK 2.0 Cookbook, Packt Publishing, and has contributed a chapter to the Bad Data Handbook, O'Reilly Media. He writes about NLTK, Python, and other technology topics at http://streamhacker.com. To demonstrate the capabilities of NLTK and natural language processing, he developed http://text-processing.com, which provides simple demos and NLP APIs for commercial use. He has contributed to various open source projects, including NLTK, and created NLTK-Trainer to simplify the process of training NLTK models. For more information, visit https://github.com/japerk/nltk-trainer.

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Nisheeth Joshi

Nisheeth Joshi

Nisheeth Joshi is an associate professor and a researcher at Banasthali University. He has also done a PhD in Natural Language Processing. He is an expert with the TDIL Program, Department of IT, Government of India, the premier organization overseeing language technology funding and research in India. He has several publications to his name in various journals and conferences, and also serves on the program committees and editorial boards of several conferences and journals.

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