Python NLTK 2.0实战：80个文本处理配方

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《Packt.Python.Text.Processing.with.NLTK.2.0.Cookbook》是一本实用指南，专为Python开发者打造，深入讲解如何利用Python的自然语言工具包（Natural Language Toolkit, NLTK）的2.0版本进行文本处理。该书汇集了超过80个具体的实践配方，旨在帮助读者最大限度地提升他们在自然语言处理（NLP）领域的技能。作者Jacob Perkins以其丰富的经验，带领读者探索NLTK库的各种功能，包括词性标注、语法分析、情感分析、文本分类、机器翻译等各个方面。书中内容不仅涵盖了基础知识，还包含了许多实际应用示例，使读者能够在项目实践中迅速上手。该书强调实战，每个章节都围绕一个特定任务或技术展开，通过逐步指导，让读者了解如何设计和实现高效、准确的NLP解决方案。同时，书中还注重代码的可读性和可复用性，以提升开发者的工作效率。版权方面，所有内容受2010年Packt Publishing的版权保护，未经许可，不得复制、存储、传播书中的任何部分，除非在学术引用或评论中进行简短摘录。尽管作者和出版社已尽力确保信息的准确性，但书中提供的所有信息均按现状销售，不附带任何形式的明示或默示保证，包括无瑕疵或适销性。对于商标，尽管Packt Publishing已尽可能正确地标记出书中提及的所有公司和产品的商标，但并不能保证所有标识的完整性。《Packt.Python.Text.Processing.with.NLTK.2.0.Cookbook》是一本极具价值的资源，适合那些希望深化理解并实践Python NLP技术的读者，无论是初学者还是经验丰富的开发人员。通过本书，读者将掌握一套全面且实用的工具，以应对日益增长的自然语言处理需求。

Preface

Errata

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Questions

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aspect of the book, and we will do our best to address it.

Tokenizing Text and WordNet Basics

WordNet is a dictionary designed for programmatic access by natural language processing

systems. NLTK includes a WordNet corpus reader, which we will use to access and explore

WordNet. We'll be using WordNet again in later chapters, so it's important to familiarize

yourself with the basics rst.

Tokenizing text into sentences

Tokenization is the process of splitting a string into a list of pieces, or tokens. We'll start by

splitting a paragraph into a list of sentences.

Getting ready

Installation instructions for NLTK are available at http://www.nltk.org/download and

the latest version as of this writing is 2.0b9. NLTK requires Python 2.4 or higher, but is not

compatible with Python 3.0. The recommended Python version is 2.6.

Once you've installed NLTK, you'll also need to install the data by following the instructions

http://www.nltk.org/data. We recommend installing everything, as we'll be using

a number of corpora and pickled objects. The data is installed in a data directory, which on

Mac and Linux/Unix is usually /usr/share/nltk_data, or on Windows is C:\nltk_data.

Make sure that tokenizers/punkt.zip is in the data directory and has been unpacked so

that there's a le at tokenizers/punkt/english.pickle.

Finally, to run the code examples, you'll need to start a Python console. Instructions on

how to do so are available at

http://www.nltk.org/getting-started. For Mac

with Linux/Unix users, you can open a terminal and type python.

How to do it...

Once NLTK is installed and you have a Python console running, we can start by creating a

paragraph of text:

>>> para = "Hello World. It's good to see you. Thanks for buying this

book."

Now we want to split para into sentences. First we need to import the sentence tokenization

function, and then we can call it with the paragraph as an argument.

>>> from nltk.tokenize import sent_tokenize

>>> sent_tokenize(para)

['Hello World.', "It's good to see you.", 'Thanks for buying this

book.']

So now we have a list of sentences that we can use for further processing.

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