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Python Text Processing with NLTK 2.0 Cookbook: LITE
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Index
Learn?LearnTextcategorizationandTopicidentification?UseStemmingandLemmatizationtogobeyondtheusualspellchecker?Replacenegationswithantonymsinyourtext?Tokenizewordsintolistsofsentencesandwords,andgainaninsightintoWordNet?Trainandmanipulatedifferenttypesofclassifiers?AutomaticallycorrectspellingwithEnchant?TranslatetextusingBabelfishAboutNaturalLanguageProcessingisusedeverywhere–insearchengines,spellcheckers,mobilephones,computergames–evenyourwashingmachine.Python'sNaturalLanguageToolkit(NLTK)suiteoflibrarieshasrapidlyemergedasoneofthemostefficienttoolsforNaturalLanguageProcessing.YouwanttoemploynothinglessthanthebesttechniquesinNaturalLanguageProcessing–andthisbookisyouranswer.PythonTextProcessingwithNLTK2.0Cookbookisyourhandyandillustrativeguide,whichwillwalkyouthroughalltheNaturalLanguageProcessingtechniquesinastep–by-stepmanner.ItwilldemystifytheadvancedfeaturesoftextanalysisandtextminingusingthecomprehensiveNLTKsuite.Tofindoutmoreaboutupgradingtothefulledition,visitwww.packtpub.com/lite-editionsandlogintoyouraccountforoffersandhelp.Ifyoudon’thaveanaccountonPacktPub.com,visittodayandsetoneup!Features?QuicklygettogripswithNaturalLanguageProcessing–withTokenizingText,WordNet,andbeyond?Learnhowtoreplaceandcorrectwordstoimprovesearchengineresults?Categorizedocumentsandpiecesoftextandassignclasslabels?PartofPackt'sCookbookseries:Eachrecipeisacarefullyorganizedsequenceofinstructionstocompletethetaskasefficientlyaspossible
目錄(43章)
倒序
- 封面
- 版權信息
- 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
- Chapter 1. Tokenizing Text and WordNet Basics
- Introduction
- Tokenizing text into sentences
- Tokenizing sentences into words
- Tokenizing sentences using regular expressions
- Filtering stopwords in a tokenized sentence
- Looking up synsets for a word in WordNet
- Looking up lemmas and synonyms in WordNet
- Calculating WordNet synset similarity
- Discovering word collocations
- Chapter 2. Replacing and Correcting Words
- Introduction
- Stemming words
- Lemmatizing words with WordNet
- Translating text with Babelfish
- Replacing words matching regular expressions
- Removing repeating characters
- Spelling correction with Enchant
- Replacing synonyms
- Replacing negations with antonyms
- Chapter 3. Text Classification
- Introduction
- Bag of Words feature extraction
- Training a naive Bayes classifier
- Training a decision tree classifier
- Training a maximum entropy classifier
- Measuring precision and recall of a classifier
- Calculating high information words
- Combining classifiers with voting
- Classifying with multiple binary classifiers
- Index 更新時間：2021-04-02 19:43:24
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