Mastering Natural Language Processing with Python - Deepti Chopra - E-Book

Mastering Natural Language Processing with Python E-Book

Deepti Chopra

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Beschreibung

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
Credits
About the Authors
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Working with Strings
Tokenization
Tokenization of text into sentences
Tokenization of text in other languages
Tokenization of sentences into words
Tokenization using TreebankWordTokenizer
Tokenization using regular expressions
Normalization
Eliminating punctuation
Conversion into lowercase and uppercase
Dealing with stop words
Calculate stopwords in English
Substituting and correcting tokens
Replacing words using regular expressions
Example of the replacement of a text with another text
Performing substitution before tokenization
Dealing with repeating characters
Example of deleting repeating characters
Replacing a word with its synonym
Example of substituting word a with its synonym
Applying Zipf's law to text
Similarity measures
Applying similarity measures using Ethe edit distance algorithm
Applying similarity measures using Jaccard's Coefficient
Applying similarity measures using the Smith Waterman distance
Other string similarity metrics
Summary
2. Statistical Language Modeling
Understanding word frequency
Develop MLE for a given text
Hidden Markov Model estimation
Applying smoothing on the MLE model
Add-one smoothing
Good Turing
Kneser Ney estimation
Witten Bell estimation
Develop a back-off mechanism for MLE
Applying interpolation on data to get mix and match
Evaluate a language model through perplexity
Applying metropolis hastings in modeling languages
Applying Gibbs sampling in language processing
Summary
3. Morphology – Getting Our Feet Wet
Introducing morphology
Understanding stemmer
Understanding lemmatization
Developing a stemmer for non-English language
Morphological analyzer
Morphological generator
Search engine
Summary
4. Parts-of-Speech Tagging – Identifying Words
Introducing parts-of-speech tagging
Default tagging
Creating POS-tagged corpora
Selecting a machine learning algorithm
Statistical modeling involving the n-gram approach
Developing a chunker using pos-tagged corpora
Summary
5. Parsing – Analyzing Training Data
Introducing parsing
Treebank construction
Extracting Context Free Grammar (CFG) rules from Treebank
Creating a probabilistic Context Free Grammar from CFG
CYK chart parsing algorithm
Earley chart parsing algorithm
Summary
6. Semantic Analysis – Meaning Matters
Introducing semantic analysis
Introducing NER
A NER system using Hidden Markov Model
Training NER using Machine Learning Toolkits
NER using POS tagging
Generation of the synset id from Wordnet
Disambiguating senses using Wordnet
Summary
7. Sentiment Analysis – I Am Happy
Introducing sentiment analysis
Sentiment analysis using NER
Sentiment analysis using machine learning
Evaluation of the NER system
Summary
8. Information Retrieval – Accessing Information
Introducing information retrieval
Stop word removal
Information retrieval using a vector space model
Vector space scoring and query operator interaction
Developing an IR system using latent semantic indexing
Text summarization
Question-answering system
Summary
9. Discourse Analysis – Knowing Is Believing
Introducing discourse analysis
Discourse analysis using Centering Theory
Anaphora resolution
Summary
10. Evaluation of NLP Systems – Analyzing Performance
The need for evaluation of NLP systems
Evaluation of NLP tools (POS taggers, stemmers, and morphological analyzers)
Parser evaluation using gold data
Evaluation of IR system
Metrics for error identification
Metrics based on lexical matching
Metrics based on syntactic matching
Metrics using shallow semantic matching
Summary
Index

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

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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:

>>> import nltk >>> text=" Welcome readers. I hope you find it interesting. Please do reply." >>> from nltk.tokenize import sent_tokenize

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

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Downloading the example code

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Errata

Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.

To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.

Piracy

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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: