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Python Text Processing with NLTK 2.0 Cookbook: LITE电子书

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作       者:Jacob Perkins

出  版  社:Packt Publishing

出版时间:2011-05-19

字       数:77.8万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NLTK will also find it useful. Students of linguistics will find it invaluable.
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Python Text Processing with NLTK 2.0 Cookbook: LITE

Table of Contents

Python Text Processing with NLTK 2.0 Cookbook: LITE

Credits

About the Author

About the Reviewers

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Errata

Piracy

Questions

1. Tokenizing Text and WordNet Basics

Introduction

Tokenizing text into sentences

Getting ready

How to do it...

How it works...

There's more...

Other languages

See also

Tokenizing sentences into words

How to do it...

How it works...

There's more...

Contractions

PunktWordTokenizer

WordPunctTokenizer

See also

Tokenizing sentences using regular expressions

Getting ready

How to do it...

How it works...

There's more...

Simple whitespace tokenizer

See also

Filtering stopwords in a tokenized sentence

Getting ready

How to do it...

How it works...

There's more...

See also

Looking up synsets for a word in WordNet

Getting ready

How to do it...

How it works...

There's more...

Hypernyms

Part-of-speech (POS)

See also

Looking up lemmas and synonyms in WordNet

How to do it...

How it works...

There's more...

All possible synonyms

Antonyms

See also

Calculating WordNet synset similarity

How to do it...

How it works...

There's more...

Comparing verbs

Path and LCH similarity

See also

Discovering word collocations

Getting ready

How to do it...

How it works...

There's more...

Scoring functions

Scoring ngrams

2. Replacing and Correcting Words

Introduction

Stemming words

How to do it...

How it works...

There's more...

LancasterStemmer

RegexpStemmer

SnowballStemmer

See also

Lemmatizing words with WordNet

Getting ready

How to do it...

How it works...

There's more...

Combining stemming with lemmatization

See also

Translating text with Babelfish

Getting ready

How to do it...

How it works...

There's more...

Available languages

Replacing words matching regular expressions

Getting ready

How to do it...

How it works...

There's more...

Replacement before tokenization

See also

Removing repeating characters

Getting ready

How to do it...

How it works...

There's more...

See also

Spelling correction with Enchant

Getting ready

How to do it...

How it works...

There's more...

en_GB dictionary

Personal word lists

See also

Replacing synonyms

Getting ready

How to do it...

How it works...

There's more...

CSV synonym replacement

YAML synonym replacement

See also

Replacing negations with antonyms

How to do it...

How it works...

There's more...

See also

3. Text Classification

Introduction

Bag of Words feature extraction

How to do it...

How it works...

There's more...

Filtering stopwords

Including significant bigrams

See also

Training a naive Bayes classifier

Getting ready

How to do it...

How it works...

There's more...

Classification probability

Most informative features

Training estimator

Manual training

See also

Training a decision tree classifier

Getting ready

How to do it...

How it works...

There's more...

Entropy cutoff

Depth cutoff

Support cutoff

See also

Training a maximum entropy classifier

Getting ready

How to do it...

How it works...

There's more...

Scipy algorithms

Megam algorithm

See also

Measuring precision and recall of a classifier

How to do it...

How it works...

There's more...

F-measure

See also

Calculating high information words

How to do it...

How it works...

There's more...

MaxentClassifier with high information words

DecisionTreeClassifier with high information words

See also

Combining classifiers with voting

Getting ready

How to do it...

How it works...

See also

Classifying with multiple binary classifiers

Getting ready

How to do it...

How it works...

There's more...

See also

Index

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