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  • Book Overview & Buying Python Text Processing with NLTK 2.0 Cookbook: LITE
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Python Text Processing with NLTK 2.0 Cookbook: LITE

Python Text Processing with NLTK 2.0 Cookbook: LITE

By : Jacob Perkins
1 (1)
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Python Text Processing with NLTK 2.0 Cookbook: LITE

Python Text Processing with NLTK 2.0 Cookbook: LITE

1 (1)
By: Jacob Perkins
Buy this Book

Overview of this book

The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward. 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.
Table of Contents (5 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Tokenizing Text and WordNet Basics
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1. Tokenizing Text and WordNet Basics
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Introduction
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Tokenizing text into sentences
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Tokenizing sentences into words
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Tokenizing sentences using regular expressions
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Filtering stopwords in a tokenized sentence
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Looking up synsets for a word in WordNet
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Looking up lemmas and synonyms in WordNet
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Calculating WordNet synset similarity
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Discovering word collocations
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2. Replacing and Correcting Words
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2. Replacing and Correcting Words
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Introduction
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Stemming words
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Lemmatizing words with WordNet
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Translating text with Babelfish
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Replacing words matching regular expressions
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Removing repeating characters
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Spelling correction with Enchant
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Replacing synonyms
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Replacing negations with antonyms
3
3. Text Classification
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3. Text Classification
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Introduction
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Bag of Words feature extraction
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Training a naive Bayes classifier
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Training a decision tree classifier
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Training a maximum entropy classifier
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Measuring precision and recall of a classifier
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Calculating high information words
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Combining classifiers with voting
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Classifying with multiple binary classifiers
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Index
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Index

Classifying with multiple binary classifiers

So far we have focused on binary classifiers, which classify with one of two possible labels. The same techniques for training a binary classifier can also be used to create a multi-class classifier, which is a classifier that can classify with one of many possible labels. But there are also cases where you need to be able to classify with multiple labels. A classifier that can return more than one label is a multi-label classifier.

A common technique for creating a multi-label classifier is to combine many binary classifiers, one for each label. You train each binary classifier so that it either returns a known label, or returns something else to signal that the label does not apply. Then you can run all the binary classifiers on your feature set to collect all the applicable labels.

Getting ready

The reuters corpus contains multi-labeled text that we can use for training and evaluation. 

>>> from nltk.corpus import reuters
>>&gt...
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Python Text Processing with NLTK 2.0 Cookbook: LITE
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