NLTK essentials : build cool NLP and machine learning applications using NLTK and other Python libraries /

If you are an NLP or machine learning enthusiast with some or no experience in text processing, then this book is for you. This book is also ideal for expert Python programmers who want to learn NLTK quickly.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Hardeniya, Nitin (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Natural language processing (Computer science)
Machine learning.
Python (Computer program language)
Natural Language Processing
Machine Learning
Traitement automatique des langues naturelles.
Apprentissage automatique.
Python (Langage de programmation)
COMPUTERS > General.
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Natural Language Processing
  • Why learn NLP?
  • Let's start playing with Python!
  • Lists
  • Helping yourself
  • Regular expression
  • Dictionaries
  • Writing Function
  • Diving into NLTK
  • Your turn
  • Summary
  • Chapter 2: Text Wrangling and Cleansing
  • What is text wrangling?
  • Text cleansing
  • Sentence splitter
  • Tokenization
  • Stemming
  • Lemmatization
  • Stop word removal
  • Rare word removalSpell correction
  • Your turn
  • Summary
  • Chapter 3: Part of Speech Tagging
  • Part of speech tagging
  • Stanford tagger
  • Deep dive into a tagger
  • Sequential tagger
  • N-grams tagger
  • Regex tagger
  • Brill tagger
  • Machine learning based tagger
  • Named Entity Recognition (NER)
  • NER tagger
  • Your Turn
  • Summary
  • Chapter 4: Parsing Structure in Text
  • Shallow versus deep parsing
  • The two approaches in parsing
  • Why we need parsing
  • Different types of parsers
  • A recursive descent parser
  • A shift-reduce parserA chart parser
  • A regex parser
  • Dependency parsing
  • Chunking
  • Information extraction
  • Named-entity recognition (NER)
  • Relation extraction
  • Summary
  • Chapter 5: NLP Applications
  • Building your first NLP application
  • Other NLP applications
  • Machine translation
  • Statistical machine translation
  • Information retrieval
  • Boolean retrieval
  • Vector space model
  • The probabilistic model
  • Speech recognition
  • Text classification
  • Information extraction
  • Question answering systems
  • Dialog systems
  • Word sense disambiguationTopic modeling
  • Language detection
  • Optical character recognition
  • Summary
  • Chapter 6: Text Classification
  • Machine learning
  • Text classification
  • Sampling
  • Naive Bayes
  • Decision trees
  • Stochastic gradient descent
  • Logistic regression
  • Support vector machines
  • The Random forest algorithm
  • Text clustering
  • K-means
  • Topic modeling in text
  • Installing gensim
  • References
  • Summary
  • Chapter 7: Web Crawling
  • Web crawlers
  • Writing your first crawler
  • Data flow in Scrapy
  • The Scrapy shellItems
  • The Sitemap spider
  • The item pipeline
  • External references
  • Summary
  • Chapter 8: Using NLTK with other Python Libraries
  • NumPy
  • ndarray
  • Indexing
  • Basic operations
  • Extracting data from an array
  • Complex matrix operations
  • Reshaping and stacking
  • Random numbers
  • SciPy
  • Linear algebra
  • eigenvalues and eigenvectors
  • The sparse matrix
  • Optimization
  • pandas
  • Reading data
  • Series data
  • Column transformation
  • Noisy data
  • matplotlib
  • Subplot
  • Adding an axis

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