Customer Reviews

Natural Language Processing with Python

5 star:		(4)
4 star:		(5)
3 star:		(3)
2 star:		(1)
1 star:		(0)

 

 

This book really delivers when it comes to code. It starts with simple tasks using the Python NLTK (Natural Language Toolkit) and builds up from there, teaching you a little bit of Python, a little bit of NLP theory, and delivering much in the way of useful applications. The author takes the time to explain the code and what is going on behind the scenes. He starts with extracting explicit words from documents and builds on that until at the end of the book you are analyzing sentence structure and building feature-based grammars.

This is not, however, an introduction to either the mathematics or information theory of natural language processing. It is not even a tutorial on Python. The book's sole purpose is to help you solve real problems using a common language without necessarily understanding the theory or the language you are using. If you really want to understand Python I suggest Learning Python. It's not as interestng as this book, but it gets the job done. To understand the theory behind natural language processing and also see how algorithms are coded up I suggest An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies).

As for this book, I think it makes a great supplement to the other books I mention and also as a recipe book of solutions to real-world problems. I really don't think it is a gentle introduction to Speech and Language Processing (2nd Edition) (Prentice Hall Series in Artificial Intelligence), as it claims to be in the preface. Currently the table of contents is not listed in the product description. I include that next for your convenience:

Chapter 1. Language Processing and Python
Section 1.1. Computing with Language: Texts and Words
Section 1.2. A Closer Look at Python: Texts as Lists of Words
Section 1.3. Computing with Language: Simple Statistics
Section 1.4. Back to Python: Making Decisions and Taking Control
Section 1.5. Automatic Natural Language Understanding
Section 1.6. Summary
Section 1.7. Further Reading
Section 1.8. Exercises
Chapter 2. Accessing Text Corpora and Lexical Resources
Section 2.1. Accessing Text Corpora
Section 2.2. Conditional Frequency Distributions
Section 2.3. More Python: Reusing Code
Section 2.4. Lexical Resources
Section 2.5. WordNet
Section 2.6. Summary
Section 2.7. Further Reading
Section 2.8. Exercises
Chapter 3. Processing Raw Text
Section 3.1. Accessing Text from the Web and from Disk
Section 3.2. Strings: Text Processing at the Lowest Level
Section 3.3. Text Processing with Unicode
Section 3.4. Regular Expressions for Detecting Word Patterns
Section 3.5. Useful Applications of Regular Expressions
Section 3.6. Normalizing Text
Section 3.7. Regular Expressions for Tokenizing Text
Section 3.8. Segmentation
Section 3.9. Formatting: From Lists to Strings
Section 3.10. Summary
Section 3.11. Further Reading
Section 3.12. Exercises
Chapter 4. Writing Structured Programs
Section 4.1. Back to the Basics
Section 4.2. Sequences
Section 4.3. Questions of Style
Section 4.4. Functions: The Foundation of Structured Programming
Section 4.5. Doing More with Functions
Section 4.6. Program Development
Section 4.7. Algorithm Design
Section 4.8. A Sample of Python Libraries
Section 4.9. Summary
Section 4.10. Further Reading
Section 4.11. Exercises
Chapter 5. Categorizing and Tagging Words
Section 5.1. Using a Tagger
Section 5.2. Tagged Corpora
Section 5.3. Mapping Words to Properties Using Python Dictionaries
Section 5.4. Automatic Tagging
Section 5.5. N-Gram Tagging
Section 5.6. Transformation-Based Tagging
Section 5.7. How to Determine the Category of a Word
Section 5.8. Summary
Section 5.9. Further Reading
Section 5.10. Exercises
Chapter 6. Learning to Classify Text
Section 6.1. Supervised Classification
Section 6.2. Further Examples of Supervised Classification
Section 6.3. Evaluation
Section 6.4. Decision Trees
Section 6.5. Naive Bayes Classifiers
Section 6.6. Maximum Entropy Classifiers
Section 6.7. Modeling Linguistic Patterns
Section 6.8. Summary
Section 6.9. Further Reading
Section 6.10. Exercises
Chapter 7. Extracting Information from Text
Section 7.1. Information Extraction
Section 7.2. Chunking
Section 7.3. Developing and Evaluating Chunkers
Section 7.4. Recursion in Linguistic Structure
Section 7.5. Named Entity Recognition
Section 7.6. Relation Extraction
Section 7.7. Summary
Section 7.8. Further Reading
Section 7.9. Exercises
Chapter 8. Analyzing Sentence Structure
Section 8.1. Some Grammatical Dilemmas
Section 8.2. What's the Use of Syntax?
Section 8.3. Context-Free Grammar
Section 8.4. Parsing with Context-Free Grammar
Section 8.5. Dependencies and Dependency Grammar
Section 8.6. Grammar Development
Section 8.7. Summary
Section 8.8. Further Reading
Section 8.9. Exercises
Chapter 9. Building Feature-Based Grammars
Section 9.1. Grammatical Features
Section 9.2. Processing Feature Structures
Section 9.3. Extending a Feature-Based Grammar
Section 9.4. Summary
Section 9.5. Further Reading
Section 9.6. Exercises
Chapter 10. Analyzing the Meaning of Sentences
Section 10.1. Natural Language Understanding
Section 10.2. Propositional Logic
Section 10.3. First-Order Logic
Section 10.4. The Semantics of English Sentences
Section 10.5. Discourse Semantics
Section 10.6. Summary
Section 10.7. Further Reading
Section 10.8. Exercises
Chapter 11. Managing Linguistic Data
Section 11.1. Corpus Structure: A Case Study
Section 11.2. The Life Cycle of a Corpus
Section 11.3. Acquiring Data
Section 11.4. Working with XML
Section 11.5. Working with Toolbox Data
Section 11.6. Describing Language Resources Using OLAC Metadata
Section 11.7. Summary
Section 11.8. Further Reading
Section 11.9. Exercises

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.

Excellent introduction to the field of Natural Language Processing. I've been using the Natural Language Toolkit, the Python library explained in this book, for about two years and have seen it continually improve and become more robust. I eagerly awaited this text, which I first learned about over a year ago, and I must say the wait was worth it. Although most useful for those with a background in computer science or linguistics, it's a fairly gentle introduction to the field, so anyone with interest in the subject should find it useful and easy to understand. Stephen, Ewan, and Edward have done an excellent job of explaining language technologies and associated algorithmic functions for analyzing text.

There are three kinds of people who might think this book could be useful:

1. Natural language processing (NLP) researchers and students who want a learn a solid programming tool to help them with their work.
2. Python programmers who want to find out more about NLP.
3. Newbies in both Python and NLP who just think the topic sounds cool and those whales on the cover are kinda cute.

In my opinion, the only kind that will find this book suitable and useful is (1). If you're familiar with Python and know no NLP it won't help you much, because it doesn't really teach NLP. It shows a few domains of this vast field, with nice code examples and all, but you should probably start with some introductory textbook on the subject or a course. You won't really learn NLP here.

The book's focus is mostly on the NLTK library written in Python by the authors. This library implements many NLP algorithms and comes with lots of data for testing and training. Almost no algorithms are implemented in the book - some are explained, and the code always imports the required modules from NLTK and shows their usage. The Python code is well-written and clean.

To conclude, if you're a NLP researcher or student, this is a very good book to read. Especially if you plan to start working with NLTK (which seems like a mature and powerful tool) - this book will serve as a great introduction. If you have other interests, this is probably not the right book.

I've only made it through the first half of the book, but here's what I think so far. It's a good book with a lot of overview information on the types of thinks that can be done with NLP today. I've certainly learned a lot. What I was disappointed by was the lack of description of the inner workings of many of the algorithms. They just give you a library and expect you to treat it as a black box. If you don't want to use their library, you have a long ways to go for real understanding.

If you have any need for Natural Language Processing - this book is the one you must read. I've bought & read the famous textbooks on the subject, but these give you just the theory & math. This book however gives you the practical know-how in order to get you started & running, in no time. It does this by using the programming language of choice for this domain (Python) & the framework of choice for doing the actual work (NLTK), which the authors have developed.
Really fun to read & very very useful, for programmers as well as anyone wishing to process texts automatically. Good also for non-programmers, as it gently introduces the programming idioms required to get the work done, & does it really well!

This book is ideal for people who are familiar with NLP and Text Mining and looking for a tool that can help implementing their ideas and doing experiments. Especially, it is a perfect fit for students and faculties doing research in this area. Some may complain what if I don't want to use Python. To my best knowledge and my years of experience in NLP and text mining, NLTK is by far the most complete toolkit for this task. Considering the short learning curve of Pytyon, even if you want to implement something in a different language eventually, it always be good to take a quick look at the data using the NLTK toolkit. Thanks a lot for the great contributions from the authors. I wish I had this book 6 years ago.

Natural Language Processing with Python is an extended tutorial using the Natural Language Toolkit (NLTK) Python library to explore Natural Language Processing (NLP) concepts. It's probably best suited for readers who already have a background in NLP but who want to learn Python/NLTK; approaching the text as an experienced programmer curious about NLP, I found it disappointing.

The authors start out well, quickly establishing a working environment and providing code examples using the NLTK library; note that you'll need Python 2.x as NLTK is not yet ported to Python 3. The library provides extensive test data and the exercises can be completed without errors.

Very early on, though, I found myself asking "why am I doing this?" as I completed a code sample. As an example, it's very nice that the NLTK library can display a dispersion plot, but what does this really tell me about the data, and more importantly, why do I need to know that? Assuming that my lack of NLP background was the problem, I continued on, only to have the text jump to a discussion of Python functions and lists. By the third chapter, I had lost interest.

I plan to review some of the background materials suggested by the authors in the "Further Reading" sections and possibly return to this book if time permits. For now it remains mostly unread, as the alternating NLP/Python discussions just weren't helpful to fully grasp either topic.

Great book for NLP. Easy to follow. However, no answer guide available either hard-copy or on-line. Boo! Hiss!

I used this book throughout a Natural Language Processing course and it helped immensely. I particularly liked having the hard copy instead of the digital version especially while working on lengthy projects and trying to understand several topics.

The sections of this book are well-defined and easy to navigate due to the bolded terminology. Great code examples are given frequently, so it is easy to follow along and grasp new concepts.

One thing you may want to know is that this book is available as a digital copy from the Python website currently. Although I prefer having a hard copy, the digital copy may be right for you.