Python Text Processing with NLTK 2.0 Cookbook: LITE - Helion
ebook
Autor: Jacob PerkinsTytuł oryginału: Python Text Processing with NLTK 2.0 Cookbook: LITE.
ISBN: 9781849516396
stron: 92, Format: ebook
Data wydania: 2011-05-13
Księgarnia: Helion
Cena książki: 69,90 zł
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Spis treści
Python Text Processing with NLTK 2.0 Cookbook: LITE eBook -- spis treści
- 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