Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit 1st Edition

by Steven Bird (Author), Ewan Klein (Author), Edward Loper (Author)

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ISBN-13: 978-0596516499

ISBN-10: 0596516495

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This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.

Packed with examples and exercises, Natural Language Processing with Python will help you:

Extract information from unstructured text, either to guess the topic or identify "named entities"
Analyze linguistic structure in text, including parsing and semantic analysis
Access popular linguistic databases, including WordNet and treebanks
Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence

This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

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	Practical Natural Language Processing	Natural Language Processing with Python	Natural Language Processing with PyTorch	Natural Language Processing with Spark NLP
Natural Language Processing from O'Reilly Media	A Comprehensive Guide to Building Real-World NLP Systems	Analyzing Text with the Natural Language Toolkit	Build Intelligent Language Applications Using Deep Learning	Learning to Understand Text at Scale

Editorial Reviews

About the Author

Steven Bird is Associate Professor in the Department of Computer Science and Software Engineering at the University of Melbourne, and Senior Research Associate in the Linguistic Data Consortium at the University of Pennsylvania. He completed a PhD on computational phonology at the University of Edinburgh in 1990, supervised by Ewan Klein. He later moved to Cameroon to conduct linguistic fieldwork on the Grassfields Bantu languages under the auspices of the Summer Institute of Linguistics. More recently, he spent several years as Associate Director of the Linguistic Data Consortium where he led an R&D team to create models and tools for large databases of annotated text. At Melbourne University, he established a language technology research group and has taught at all levels of the undergraduate computer science curriculum. In 2009, Steven is President of the Association for Computational Linguistics.

Ewan Klein is Professor of Language Technology in the School of Informatics at the University of Edinburgh. He completed a PhD on formal semantics at the University of Cambridge in 1978. After some years working at the Universities of Sussex and Newcastle upon Tyne, Ewan took up a teaching position at Edinburgh. He was involved in the establishment of Edinburgh's Language Technology Group in 1993, and has been closely associated with it ever since. From 2000-2002, he took leave from the University to act as Research Manager for the Edinburgh-based Natural Language Research Group of Edify Corporation, Santa Clara, and was responsible for spoken dialogue processing. Ewan is a past President of the European Chapter of the Association for Computational Linguistics and was a founding member and Coordinator of the European Network of Excellence in Human Language Technologies (ELSNET).

Edward Loper has recently completed a PhD on machine learning for natural language processing at the the University of Pennsylvania. Edward was a student in Steven's graduate course on computational linguistics in the fall of 2000, and went on to be a TA and share in the development of NLTK. In addition to NLTK, he has helped develop two packages for documenting and testing Python software, epydoc, and doctest.

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Product details

Publisher ‏ : ‎ O'Reilly Media; 1st edition (August 4, 2009)
Language ‏ : ‎ English
Paperback ‏ : ‎ 502 pages
ISBN-10 ‏ : ‎ 0596516495
ISBN-13 ‏ : ‎ 978-0596516499
Item Weight ‏ : ‎ 1.75 pounds
Dimensions ‏ : ‎ 7 x 1.2 x 9.19 inches

Best Sellers Rank: #51,486 in Books (See Top 100 in Books)
- #12 in Natural Language Processing (Books)
- #15 in JavaScript Programming (Books)
- #26 in Programming Algorithms

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170 ratings

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Steven Bird
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Associate Professor, Department of Computer Science and Software Engineering, University of Melbourne; Senior Research Scientist, International Computer Science Institute, University of California Berkeley.
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Ewan Klein
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John M. Ford

Questionable Material

Reviewed in the United States on November 12, 2014

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Edward Loper's book is an introduction to the Natural Language Toolkit (NLTK) for the Python programming language. Its target audience is a narrow one. It assumes a working familiarity with Python. It's true that an experienced programmer could learn Python along the way, but getting the most from the code examples and walkthrough explanations requires enough familiarity to "think" in Python. The book also assumes sufficient familiarity with Natural Language Processing (NLP) to understand why one would want to build lexicons, grammars, and parsers.

The book has several strengths. It is tightly integrated with Python and NLTK code. There are numerous examples throughout and the author walks through and modifies them to clarify how the NLTK works. The sizeable reference sections at the end of each chapter are also valuable. These sections include both introductory and advanced sources. And a lot of them. There is also useful integration with the NLTK web site which provides and points to additional resources.

Not to be missed are the end-of-chapter questions. Readers have come to expect little from these learning aids; they usually invite us to parrot back a small number of key concepts or try a few calculations or code segments. This book's questions go far beyond the norm. They introduce new concepts, encourage writing and comparing several versions of a program, and otherwise extend each chapter's contents. Even readers who don't plan to complete these exercises should read them closely.

Weaknesses are few. As noted, the book may assume too much Python and NLP background for some users. It does have a narrow focus and is not organized the right way to be used as a reference book. Readers who want something a little more modular and reference-like might prefer Jacob Perkins' Python 3 Text Processing with NLTK 3 Cookbook . David Mertz's Text Processing in Python is an older source, but still useful as well.

23 people found this helpful

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Pouria Babvey

It is still useful

Reviewed in the United States on September 14, 2018

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I am working on social network analysis and text mining. Of course, anyone can find the code pieces on the internet but this book provides a good framework for whoever wants to start working on text mining. The book is somewhat old (2009). For computer programming, even a year may make a book obsolete. However, the NLTK package is still one of the best beside other competitors like SpaCy. One important topic that the book does not cover is vectorization which is a relatively new topic. Overall, I enjoyed reading the book. It was well-organized and to the point for whatever you want to apply text mining to.

9 people found this helpful

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Jack Sparrow

Good book, great library

Reviewed in the United States on September 21, 2009

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Buy this book only if you:
1. Know the basics of natural language processing (NLP) or linguistics;
2. Know the Python programming language or you're willing to learn it;
3. Are using the NLTK library or plan to do so.

NLTK is a Python library that offers many standard NLP tools (tokenizers, POS taggers, parsers, chunkers and others). It comes with samples of several dozens of text corpora typically used in NLP applications, as well as with interfaces to dictionary-like resources such as WordNet and VerbNet. No FrameNet, though. NLTK is well documented, so you might not need this book initially. However, it definitely helps to have it on your desk if you are serious about using NLTK.

The first chapters are a bit messy, as they attempt to introduce all three themes (NLP, NLTK and Python) together. Beginners may have some difficulty sorting things out. By the time you reach the WordNet section, you either got lost in the forest, realize that you would never understand this topic without the book, or both. However, if you are a bit patient and try out all simple code examples, you'll make it eventually. In my opinion, NLTK remains the simplest, most elegant and well rounded library of its kind.

61 people found this helpful

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Sujit Pal

Great guide to NLTK

Reviewed in the United States on December 26, 2012

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NLTK was originally designed for teaching NLP, but because NLTK is so comprehensive, it is also quite vast, and you will need a guide to get you started on using it effectively. This book is that guide. Its organized into 4 sections. Chapters 1-4 cover the basics; 5-7 covers language processing, tagging, classification and information extraction; Chapters 8-10 covers sentence parsing, syntax, structure and representations of meaning, and Chapter 11 covers managing linguistics data. If you are looking to get an overview of NLP, as well as reasonable proficiency in manipulating text and extracting information from it, this book may be for you. People who are not NLP specialists but need to use NLP techniques at work will find this book particularly helpful. People who plan on specializing in NLP will probably find the book useful as a stepping stone into the field.

My interest in NLP (and the book) is limited to being able to apply machine learning techniques to solve NLP problems, so I found the first two sections really useful. However, the entire book (including the exercises) is a great source of ideas on what you can accomplish in NLP with NLTK.

8 people found this helpful

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David Knickerbocker

and it's so easy to jump right in and play with the NLTK

Reviewed in the United States on August 31, 2017

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This is one of the most interesting programming books I have ever read, and it's so easy to jump right in and play with the NLTK. I have devoured this book. A lot of the code examples in the book no longer work as written, as the library has changed over the last eight years, but it is easy enough to Google the new way, and it is educational trying to figure out how to get things working that aren't well documented. Great book.

5 people found this helpful

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Amazon Customer

Good starting place, but outdated

Reviewed in the United States on January 19, 2021

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Offers a good introduction to the topic, but could seriously do with an update. If you use this book, plan to devote about 15 minutes every hour to troubleshooting outdated code. It begins to get bad around the second half of chapter 2, and by chapter 3 there's an error with almost every other example code segment.

One person found this helpful

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Alan

Good intro book to NLP in Python

Reviewed in the United States on November 5, 2019

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Very good book to start with on NLP. I wanted to write a program quickly that can parse through some text data and generate some statistics of the words contained. The codes in the book can be used directly to complete the task. With the help of some packages introduced in this book, my task turned out to be a lot easier than I expected.

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Zack

Needs an update, 2009 is a long time ago in tech

Reviewed in the United Kingdom on July 21, 2017

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The book is written reasonably well, except the last couple of chapters that go off the rails in terms of sparsity of explanations. The major drawback of this book is unfortunately that it hasn't had a recent update and therefore doesn't have much to offer if you're looking for modern techniques involving machine learning.

6 people found this helpful

amc

Outdated

Reviewed in the United Kingdom on September 6, 2020

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Nlp has moved on a lot in recent years. This book is a bit outdated nowadays

2 people found this helpful

Great for students

Reviewed in the United Kingdom on March 17, 2014

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I have been using this book to help me with my final year project on text mining in a Computer Science course, and I love it! It was overwhelming at first because I was brand new to Python and natural language processing, but after I learnt a bit more about the topics the book became very helpful for me and I use it almost every day at the moment.

Lewis Reed

a very good way into the ideas and methods of NLP

Reviewed in the United Kingdom on February 14, 2015

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This book is intended for the use and instruction of NLTK, but it also provides a nice overview of some of the issues of processing natural language and explains some general methods used to deal with them. Further reading is also suggested and relevant extra materials are available online.

Hussain Alzubaidi

good

Reviewed in the United Kingdom on February 23, 2020

Verified Purchase

good book

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Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit 1st Edition

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