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Natural Language Processing with Python
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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 us
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Paperback, 504 pages
Published
July 7th 2009
by O'Reilly Media
(first published January 1st 2009)
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Start your review of Natural Language Processing with Python
Dec 08, 2011
Manny
rated it
really liked it
Recommends it for:
People who want to write software that analyses text
[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 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 ...more
HAMLET: Words, words, words.
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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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.
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
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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
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
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.
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.
This subject fascinates me and I worked through this book front to back as quickly as I could. This is a complicated topic, and though the earlier chapters can inform shallow solutions that will certainly impress, the problem space is vast. There are a few reviews out there claiming that the book is "outdated"--please do not let that stop you from picking it up. It's an excellent in-depth manual for Python NLP practitioners
Warning: if you haven't been exposed to a typical CS undergrad course loa ...more
Warning: if you haven't been exposed to a typical CS undergrad course loa ...more
Good intro to linguistics, NLP, and the open source NLTK python toolkit
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.
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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
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
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
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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
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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.
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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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.
The now ubiquitous science of "natural language processing", whose applications include translation, autocorrect systems, search engines, chat bots, and much more, is here explained in a uniquely clear and pedagogic approach, requiring only a beginner's understanding of Python.
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Really comprehensive overview of NLP techniques and methodology, as well as a look into the NLTK. Not a great reference text, but very good for getting an awareness of how to solve a wide range of NLP problems.
At this point, a lot of the content in this book is outdated. However, it is a clear read, and it covers the fundamentals. Now I must go on to find a more recent and specialized book on information extraction.
It explain the basic concept about NLP toolkit.
good practice for NLP scientist
Easy to learn
a bit dense, just enough theory. covers a lot of topics
Very complete.
I really liked it and recommend it for writers at all levels.
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 ...more
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 ...more
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
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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.
some of the examples need tweaking since the book is from 2009 some things have changed within the nltk library.
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!
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!
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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“>>> 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']”
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“>>> 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']”
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