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Natural Language Processing Fundamentals
IfNLPhasn'tbeenyourforte,NaturalLanguageProcessingFundamentalswillmakesureyousetofftoasteadystart.ThiscomprehensiveguidewillshowyouhowtoeffectivelyusePythonlibrariesandNLPconceptstosolvevariousproblems.You'llbeintroducedtonaturallanguageprocessinganditsapplicationsthroughexamplesandexercises.Thiswillbefollowedbyanintroductiontotheinitialstagesofsolvingaproblem,whichincludesproblemdefinition,gettingtextdata,andpreparingitformodeling.Withexposuretoconceptslikeadvancednaturallanguageprocessingalgorithmsandvisualizationtechniques,you'lllearnhowtocreateapplicationsthatcanextractinformationfromunstructureddataandpresentitasimpactfulvisuals.AlthoughyouwillcontinuetolearnNLP-basedtechniques,thefocuswillgraduallyshifttodevelopingusefulapplications.Inthesesections,you'llunderstandhowtoapplyNLPtechniquestoanswerquestionsascanbeusedinchatbots.Bytheendofthisbook,you'llbeabletoaccomplishavariedrangeofassignmentsrangingfromidentifyingthemostsuitabletypeofNLPtaskforsolvingaproblemtousingatoollikespacyorgensimforperformingsentimentanalysis.Thebookwilleasilyequipyouwiththeknowledgeyouneedtobuildapplicationsthatinterprethumanlanguage.
目錄(68章)
倒序
- 封面
- 版權頁
- Preface
- About the Book
- Chapter 1 Introduction to Natural Language Processing
- Introduction
- History of NLP
- Text Analytics and NLP
- Various Steps in NLP
- Kick Starting an NLP Project
- Summary
- Chapter 2 Basic Feature Extraction Methods
- Introduction
- Types of Data
- Cleaning Text Data
- Feature Extraction from Texts
- Feature Engineering
- Summary
- Chapter 3 Developing a Text classifier
- Introduction
- Machine Learning
- Developing a Text Classifier
- Building Pipelines for NLP Projects
- Saving and Loading Models
- Summary
- Chapter 4 Collecting Text Data from the Web
- Introduction
- Collecting Data by Scraping Web Pages
- Requesting Content from Web Pages
- Dealing with Semi-Structured Data
- Summary
- Chapter 5 Topic Modeling
- Introduction
- Topic Discovery
- Topic Modeling Algorithms
- Summary
- Chapter 6 Text Summarization and Text Generation
- Introduction
- What is Automated Text Summarization?
- High-Level View of Text Summarization
- TextRank
- Summarizing Text Using Gensim
- Summarizing Text Using Word Frequency
- Generating Text with Markov Chains
- Summary
- Chapter 7 Vector Representation
- Introduction
- Vector Definition
- Why Vector Representations?
- Summary
- Chapter 8 Sentiment Analysis
- Introduction
- Why is Sentiment Analysis Required?
- Growth of Sentiment Analysis
- Tools Used for Sentiment Analysis
- TextBlob
- Understanding Data for Sentiment Analysis
- Training Sentiment Models
- Summary
- Appendix
- Chapter 1: Introduction to Natural Language Processing
- Chapter 2: Basic Feature Extraction Methods
- Chapter 3: Developing a Text classifier
- Chapter 4: Collecting Text Data from the Web
- Chapter 5: Topic Modeling
- Chapter 6: Text Summarization and Text Generation
- Chapter 7: Vector Representation
- Chapter 8: Sentiment Analysis 更新時間：2021-06-11 13:42:47
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