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Python Text Processing with NLTK 2.0 Cookbook: LITE

Jacob Perkins

Ebook

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...
    - Theres 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

Tytuł: Python Text Processing with NLTK 2.0 Cookbook: LITE
Autor: Jacob Perkins
Tytuł oryginału: Python Text Processing with NLTK 2.0 Cookbook: LITE.
ISBN: 9781849516396, 9781849516396
Data wydania: 2011-05-13
Format: Ebook
Identyfikator pozycji: e_3bao
Wydawca: Packt Publishing

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