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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Maximize your NLP capabilities while creating amazing NLP projects in Python
About This Book
- Learn to implement various NLP tasks in Python
- Gain insights into the current and budding research topics of NLP
- This is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applications
Who This Book Is For
This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.
What You Will Learn
- Implement string matching algorithms and normalization techniques
- Implement statistical language modeling techniques
- Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
- Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
- Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
- Develop an NER-based system and understand and apply the concepts of sentiment analysis
- Understand and implement the concepts of Information Retrieval and text summarization
- Develop a Discourse Analysis System and Anaphora Resolution based system
In Detail
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.
This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.
You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.
Style and approach
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.
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Seitenzahl: 204
Veröffentlichungsjahr: 2016
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Table of Contents
Mastering Natural Language Processing with Python
Mastering Natural Language Processing with Python
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: June 2016
Production reference: 1030616
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78398-904-1
www.packtpub.com
Credits
Authors
Deepti Chopra
Nisheeth Joshi
Iti Mathur
Reviewer
Arturo Argueta
Commissioning Editor
Pramila Balan
Acquisition Editor
Tushar Gupta
Content Development Editor
Merwyn D'souza
Technical Editor
Gebin George
Copy Editor
Akshata Lobo
Project Coordinator
Nikhil Nair
Proofreader
Safis Editing
Indexer
Hemangini Bari
Graphics
Jason Monteiro
Production Coordinator
Manu Joseph
Cover Work
Manu Joseph
About the Authors
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.
Nisheeth Joshi works as an Associate Professor at Banasthali University. His areas of interest include computational linguistics, Natural Language Processing, and artificial intelligence. Besides this, he is also very actively involved in the development of MT engines for English to Indian languages. He is one of the experts empaneled with the TDIL program, Department of Information Technology, Govt. of India, a premier organization that oversees Language Technology Funding and Research in India. He has several publications in various journals and conferences and also serves on the program committees and editorial boards of several conferences and journals.
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.
We acknowledge with gratitude and sincerely thank all our friends and relatives for the blessings conveyed to us to achieve the goal to publishing this Natural Language Processing-based book.
About the Reviewer
Arturo Argueta is currently a PhD student who conducts High Performance Computing and NLP research. Arturo has performed some research on clustering algorithms, machine learning algorithms for NLP, and machine translation. He is also fluent in English, German, and Spanish.
www.PacktPub.com
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Preface
In this book, we will learn how to implement various tasks of NLP in Python and gain insight to the current and budding research topics of NLP. This book is a comprehensive step-by-step guide to help students and researchers to create their own projects based on real-life applications.
What this book covers
Chapter 1, Working with Strings, explains how to perform preprocessing tasks on text, such as tokenization and normalization, and also explains various string matching measures.
Chapter 2, Statistical Language Modeling, covers how to calculate word frequencies and perform various language modeling techniques.
Chapter 3, Morphology – Getting Our Feet Wet, talks about how to develop a stemmer, morphological analyzer, and morphological generator.
Chapter 4, Parts-of-Speech Tagging – Identifying Words, explains Parts-of-Speech tagging and statistical modeling involving the n-gram approach.
Chapter 5, Parsing – Analyzing Training Data, provides information on the concepts of Tree bank construction, CFG construction, the CYK algorithm, the Chart Parsing algorithm, and transliteration.
Chapter 6, Semantic Analysis – Meaning Matters, talks about the concept and application of Shallow Semantic Analysis (that is, NER) and WSD using Wordnet.
Chapter 7, Sentiment Analysis – I Am Happy, provides information to help you understand and apply the concepts of sentiment analysis.
Chapter 8, Information Retrieval – Accessing Information, will help you understand and apply the concepts of information retrieval and text summarization.
Chapter 9, Discourse Analysis – Knowing Is Believing, develops a discourse analysis system and anaphora resolution-based system.
Chapter 10, Evaluation of NLP Systems – Analyzing Performance, talks about understanding and applying the concepts of evaluating NLP systems.
What you need for this book
For all the chapters, Python 2.7 or 3.2+ is used. NLTK 3.0 must be installed either on a 32-bit machine or 64-bit machine. The operating system that is required is Windows/Mac/Unix.
Who this book is for
This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "For tokenization of French text, we will use the french.pickle file."
A block of code is set as follows:
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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Questions
If you have a problem with any aspect of this book, you can contact us at <[email protected]>, and we will do our best to address the problem.
Normalization
In order to carry out processing on natural language text, we need to perform normalization that mainly involves eliminating punctuation, converting the entire text into lowercase or uppercase, converting numbers into words, expanding abbreviations, canonicalization of text, and so on.
Eliminating punctuation
Sometimes, while tokenizing, it is desirable to remove punctuation. Removal of punctuation is considered one of the primary tasks while doing normalization in NLTK.
Consider the following example:
