Деталі електронної книги

Python Text Processing with NLTK 2.0 Cookbook: LITE

Python Text Processing with NLTK 2.0 Cookbook: LITE

Jacob Perkins

Eлектронна книга
  • 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
  • Назва: Python Text Processing with NLTK 2.0 Cookbook: LITE
  • Автор: Jacob Perkins
  • Оригінальна назва: Python Text Processing with NLTK 2.0 Cookbook: LITE.
  • ISBN: 9781849516396, 9781849516396
  • Дата видання: 2011-05-13
  • Формат: Eлектронна книга
  • Ідентифікатор видання: e_3bao
  • Видавець: Packt Publishing