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Natural Language Processing with Python

Steven Bird (Goodreads Author),

Ewan Klein,

Edward Loper

4.10 · Rating details · 496 ratings · 43 reviews

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 us ...more

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Paperback, 504 pages

Published July 7th 2009 by O'Reilly Media (first published January 1st 2009)

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Natural Language Processing with Python by Steven Bird

Introduction to Information Retrieval by Christopher D. Manning

Foundations of Statistical Natural Language Processing by Christopher Manning

The Geometry of Information Retrieval by C.J. van Rijsbergen

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Dec 08, 2011 Manny rated it really liked it

Recommends it for: People who want to write software that analyses text

Shelves: linguistics-and-philosophy, well-i-think-its-funny, science, what-i-do-for-a-living

[Editor's preface to the second edition: notgettingenough read the first edition of this review and complained that it was all Geek to her. I have amended it accordingly]

POLONIUS: What do you read, my lord?
HAMLET: Words, words, words.

Hamlet was evidently interested in textual analysis, and if the Python Natural Language Toolkit (NLTK) had been available in Elsinore I'm sure he'd have bought this book too. I'd heard good things about it, and it doesn't disappoint: the authors have done a terrific job of combining a lot of freeware tools and resources into a neat package.

They say they want it to be accessible even to people who have no software development experience; this may be just a little optimistic, but try it for yourself and see what you think. They've certainly made every effort to get you hooked from the beginning. Ten minutes after downloading the software, I was able to produce a randomized version of Monty Python and the Holy Grail with a single command:

>>>Python 2.6.6 
>>> import nltk
>>> nltk.download()
>>> from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
>>> text6.generate()
Building ngram index...
SCENE 1 : Well , I see . Running away , And his nostrils 
raped and his bottom burned off , And his pen -- SIR ROBIN 
: We are just not used to handsome knights . Nay . Nay . 
Come on . Anybody armed must go too . OFFICER # 1 : No . 
Not only by surprise . Not only by surprise . Not the 
Knights Who Say ' Ni '. KNIGHTS OF NI : Ni ! ARTHUR : 
You know much that is . Yeah , a swallow ' s got a point . 
SOLDIER #>>>

So what else can it do? Geeks may want to skip to the example below, but here's a brief summary. The toolkit contains three kinds of materials. First, there's a well-selected set of texts, packaged up so that they can easily be used. Some of them are listed above; there are a couple of dozen more that you can quickly locate.

Second, there's a bunch of tools which you can use to analyze the texts. For example, there's an interface to WordNet, which is a kind of digitized super-thesaurus containing tens of thousands of words and concepts, all neatly arranged into a complex hierarchy with the most general concepts at the top and the most specific ones at the bottom. There's a tool called a "part-of-speech tagger", which takes a pieces of text and guesses the part of speech - noun, verb, adjective, etc - for each word in the context in which it appears. There are "parsers", which can analyze sentences in terms of grammatical function - finding subjects, objects, main verbs, and so on. And there are plenty of other things, in particular easy ways to incorporate machine learning methods, which you can train yourself by giving them examples.

Third, there's Python itself, which is the glue that sticks all these things together. I'd somehow never used Python before, but it's a concise and elegant language that's easy to learn if you already have some software skills. If you know Perl, Ruby, Unix shell-scripting, or anything like that, you'll be up and flying in no time. You can write scripts which are just a few lines long, but which do a whole lot of stuff: read a file from the web, chop it up into individual words and sentences, find all the sentences that have some particular property you're searching for, and then display everything as a neat table or graph.

The rest of the review will probably only be interesting to geeks, but if that's you, please read on...
________________________________

I finished the book yesterday, and I've just spent a few hours messing around writing little scripts to see what it can do. Here's the most entertaining one. I thought it would be interesting to be able to locate all the words in a text that refer to animals. NLTK includes a handy interface to WordNet, so the first job was to write a function which checks whether a word could refer to a concept lower in the hierarchy than the one for 'animal'. It's never quite as easy as you first think; after a little experimentation, I realized that I had to block words which referred to animals only by virtue of referring to human beings. The final definition looks like this:

animal_synset = wn.synset('animal.n.01')
human_synset = wn.synset('homo.n.02')

def is_animal_word(word):
 hypernyms = [ hyp
 for synset in wn.synsets(word)
 for path in synset.hypernym_paths()
 for hyp in path
 if not human_synset in path]
 return animal_synset in hypernyms

I then wrote a script which called my function to return all the animal words in the first n words of a piece of text:

def print_animal_words_v1(text, n):
 words = set([w.lower() for w in text[:n]])
 animal_words = sorted(set([w for w in words
 if is_animal_word(w)]))
 print "Animal words in first %d words" % n
 print(animal_words)

They've packaged up a bunch of textual resources for easy access, so I could immediately test it on the first 50,000 words of Emma:

>>> emma = gutenberg.words('austen-emma.txt')
>>> print_animal_words_v1(emma, 50000)
Animal words in first 50000 words
['baby', 'bear', 'bears', 'blue', 'chat', 'chicken', 
'cow', 'cows', 'creature', 'creatures', 'does', 
'entire', 'female', 'fish', 'fly', 'games', 'goose', 
'head', 'horse', 'horses', 'imagines', 'kite', 
'kitty', 'martin', 'martins', 'monarch', 'mounts', 
'oysters', 'pen', 'pet', 'pollards', 'shark', 
'sharks', 'stock', 'tumbler', 'young']

A quick look at this reveals some suspicious candidates: for example, 'does' is most likely never used as the plural of 'doe', so shouldn't be counted as an animal word.

My second version of the script called another resource, a "tagger", which quickly goes through the text and tries to guess what part of speech each word is in the context in which it appears. I only look at the words whose tags start with an 'N', indicating that they have been guessed as nouns:

def print_animal_words_v2(text, n):
 print "Tagging first %d words" % n
 tagged_words = nltk.pos_tag(text[:n])
 print("Tagging done")
 words = set([w.lower() for (w, tag) in tagged_words
 if tag.startswith('N')])
 animal_words = sorted(set([w for w in words
 if is_animal_word(w)]))
 print "Animal words in first %d words" % n
 print(animal_words)

Now I get a shorter list, which in particular omits the suspicious 'does':

>>> print_animal_words_v2(emma, 50000)
Tagging first 50000 words
Tagging done
Animal words in first 50000 words
['baby', 'bears', 'blue', 'chicken', 'cow', 
'creature', 'creatures', 'female', 'games', 'goose', 
'head', 'horse', 'horses', 'kitty', 'martin', 
'martins', 'monarch', 'oysters', 'pet', 'pollards', 
'shark', 'sharks', 'stock', 'tumbler', 'young']

Well, that should be enough to give you the favor of the thing. If you don't want to buy the book, it's available free online here. Have fun!
...more

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Aug 27, 2014 Ji rated it really liked it

Shelves: technical

I'm sure I'll come back to this book again, and again. It's a truly good one - not only I got to start learning the basics of text mining in Python using nltk, but also I learned some basics of Python data processing ideas and routines. It's a shame that with my limited knowledge so far, I can only devour very little value out of reading it. Definitely a five star!

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Mar 23, 2010 David rated it it was amazing

Shelves: computer-science, programming, linguistics, natural-language-processing, reference, non-fiction

Excellent intro to both Python programming and NLP. Assumes no prior familiarity with either, so this is a good book both for beginning CS students who know little to nothing about linguistics, and for beginning linguists who have no programming experience.

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Oct 12, 2016 Muhammad al-Khwarizmi rated it really liked it

Shelves: linguistics, artificial-intelligence, computation, statistics

Really very decent introduction to this particularly library. A big caveat is that it contains a fairly large number of typos, even really obvious ones like "ibigrams", and includes some code that no longer functions with the current iteration of NLTK. Another issue, at least from my point of view, is that it isn't really geared towards a streamlined approach to using NLTK as a tool for text mining, with its numerous digressions into how to use Python for those entirely unfamiliar with it, as a ...more

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Apr 22, 2018 Shubhang Goswami rated it really liked it

Shelves: path-to-ai-and-ml

A guide book on the NLTK toolkit that allows you to dissect language and make a computer understand language.

The authors build up from very simple models to complex ones as the book progresses, clearly laying down a story in front of us.

If you are well verse with python you can skip the first 4 chapters and head straight to chapter 5. I liked this book and it gave me an idea of how google assistant might parse data when I ask it to do something for me. Makes you look at language differently.

Wo ...more

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Mar 06, 2018 Alex rated it liked it

Shelves: technical-reference

A solid resource that feels a bit overdue for an update. Much of the core linguistic information is still relevant I am sure, but there is no coverage of newer ML techniques (word embeddings, RNNs, etc.), and the Python NLTK library itself has been updated extensively enough since this was published to render many of the code examples incompatible with the current version. For those pursuing grammar-based approaches to NLP, though, this should still prove a useful reference.

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Aug 15, 2010 Rob Young rated it really liked it

This is a fascinating book. Everything from text processing to statistics to lexical analysis. For most problems the solution is shown in both set theory notation and python making it much easier for a programmer to understand the theory.

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Jul 28, 2020 Nedret Efe rated it really liked it

Good intro to linguistics, NLP, and the open source NLTK python toolkit

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May 10, 2014 Dave Peticolas rated it liked it

A wonderful introduction to natural language processing using the NLTK toolkit. This book also serves as an introduction to Python for those new to the language (and to programming, though the pace is pretty fast). And for those like me, not new to Python but totally ignorant of NLP, it contains a wealth of interesting material. Finally, the examples showcase the elegance of Python as a language for text processing. ...more

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Jun 21, 2017 E M rated it really liked it

This excellent work moves the novice from the basics of natural language processing (NLP) into advanced topics with dexterity. NLP is the discipline of interpreting language as humans produce it with computational tools that computers require. By the time you complete this book you will be able to incorporate NLP into your data science workflow.

There are several things I love about this book. It strikes an excellent balance between theory and applications. It provides compelling use cases along ...more

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Apr 16, 2019 Florian Hopf rated it really liked it

Goes into detail on a lot of different topics that can be relevant when processing natural language like tokenization, parts of speech tagging, grammars, logic, data formats and machine learning aspects, including many excercises. Sometimes it feels like it spans too many areas, from programming beginner chapters to more complex topics. I have to admit that reading was painful from time to time - it's a quite long book. But even though I considered giving only 3 stars (mainly due to the large am ...more

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Dec 03, 2017 Ankit Solanki rated it liked it

Shelves: read-in-college

The books even though lost generality as a whole but still shows the working of NLP algorithms much clearly then other website or blogs, it certainly give me a better vision of natural language processing. The approach of this book was to state and explain with a hell lot of examples and it was indeed a good idea. The last 2 chapters were really complex and hard to understand but I'm sure if I'll read those topics again and the insight gained in this book will help me a lot in it. If you are als ...more

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May 11, 2017 Jesús Navarro rated it really liked it

A very clear, simple and comprehensive book on the fundamentals of NLP, the techniques and theory involved, with practical examples. Covers tokenization, tagging, parsing, information extraction, classification, syntactic and semantic analysis.

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Jan 15, 2018 Vilém Zouhar rated it it was ok

My disappointment has roots in my expectation of theoretical introduction to Natural Language Processing. I'm not pressing this book is not enjoable, but that's not the case only if you want 500 pages of Python nltk examples.

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Mar 20, 2018 Wasim Khan rated it really liked it

This book introduces both Natural Language Processing Toolkit and Natural Language Processing and it's a good book at that. Both theory and code examples are thrown in good measure. It's a must if you want to have NLP concepts before jumping to NLP packages.

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Aug 31, 2019 Alfia rated it really liked it · review of another edition

Accessible intro to NLP concepts and practice in Python 2.7. The print version includes some deprecated code so be sure to check the Wiley site and Stack Overflow for updates. Not sure when the ereader version was last updated but it is moreso than the print.

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Oct 16, 2017 Ivan Atanasov rated it liked it

It explain the basic concept about NLP toolkit.

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Apr 25, 2018 Fatma Fatma rated it it was amazing

good practice for NLP scientist

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Nov 22, 2018 Mahmoud El rated it really liked it

Easy to learn

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Apr 02, 2019 Jason Kraft rated it it was amazing

a bit dense, just enough theory. covers a lot of topics

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Jul 22, 2019 Laura rated it it was ok

I really liked it and recommend it for writers at all levels.

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Oct 21, 2019 Echo rated it really liked it

Shelves: read-in-2019

A bit repetitive at times and I wish there were solutions for the exercises, at least those, where there's a correct solution.

Otherwise it's a great resource, which sometimes goes to far into detail and sometimes not enough. But you'll definitely learn about the most important features of NLTK

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Sep 07, 2014 David Rauschenbach rated it it was amazing

In my 24 years as a software engineer, this is the best technical book I have ever read. I It represents the state of the art in technical writing style. Where most technical books age quickly, this one is surprisingly open-ended, and tied to an external developer toolkit that is both wonderful and growing. The subject matter is structured (readable as a narrative, then good for reference), thoroughly treated (by Stanford alumns), and grounded in historical references and philosophical themes. I ...more

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Mar 15, 2016 Atti rated it it was amazing

full of interesting ideas will walk you through basic python development, text processing and machine learning. There is a lot to take in with this book, i can see myself returning to it in the future to use it as a reference.

some of the examples need tweaking since the book is from 2009 some things have changed within the nltk library.

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Jul 11, 2016 K.C. Hopson added it · review of another edition

Comprehensive intro

While, I was not interested in learning Python, I did want to learn the state of the art in NLP. This book does the latter better than anything this side of a college textbook, from what I can see. If you want to learn Python, this book is an incredible start also!

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May 31, 2012 Nick rated it it was amazing

Such an interesting problem, and such a fun programming language. The NLTK looks great and I certainly hope to put it to good use soon. This book was very clear and easy to follow, and is a great resource I'm sure I'll be referring back to before too long.

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Jun 27, 2013 Christopher rated it really liked it

Nice introduction to NLP.
It also serves as a intro to Python for some reason. Probably nice for someone new to the language.

The best part is the introduction to the Natural Language Toolkit (nltk) python library which is a great way to get started with and explore NLP.

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Aug 12, 2013 JDK1962 rated it it was amazing

Excellent. Certainly wish I had this book when I took "Natural Language Processing" back in grad school. Good intro to the topic of NLP, and you wind up being able to actually do things in Python when you're finished.

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Jul 27, 2015 Daniel Svoboda rated it it was amazing · review of another edition

Really good and concise introduction to Natural Language Processing and the indispensable NLTK library for doing your own NLP projects. O'Reilly has again given a great technical book that reads like literature.

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Nov 10, 2015 Vincent Russo rated it it was amazing

Shelves: data-science

This series of books are great. For the readers that already known Python, it's very clear which sections of the book can be glossed over to focus on the NLP portions. Lots of very practical examples that make use of the NLTK corpus, a resource that is actually quite useful to test on.

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“>>> puzzle_letters = nltk.FreqDist('egivrvonl') >>> obligatory = 'r' >>> wordlist = nltk.corpus.words.words() >>> [w for w in wordlist if len(w) >= 6 ... and obligatory in w ... and nltk.FreqDist(w) <= puzzle_letters] ['glover', 'gorlin', 'govern', 'grovel', 'ignore', 'involver', 'lienor', 'linger', 'longer', 'lovering', 'noiler', 'overling', 'region', 'renvoi', 'revolving', 'ringle', 'roving', 'violer', 'virole']” — 1 likes

“>>> wn.synset('car.n.01').definition 'a motor vehicle with four wheels; usually propelled by an internal combustion engine' >>> wn.synset('car.n.01').examples ['he needs a car to get to work']” — 0 likes

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